Oscar Argudo

Plant ecosystems are an essential element in bringing virtual worlds to life. However, current modeling methods do not scale well to large virtual environments due to the complexity of the interactions between individual trees required to accurately model realistic plant distributions. The resulting lengthy run-times also hamper authoring. To address the twin demands of scalability and interactive authoring, we introduce a hybrid grid-based simulation that distinguishes between the individual modeling of mature trees, whose interactions are encoded with convolution-like operations, and the aggregate modeling of small trees (encompassing saplings and bushes), which are encoded using a per-cell mean-tree representation. Our simulation model is well-suited to parallelization and operates efficiently on graphics hardware, outperforming existing ecosystem simulation approaches by three orders of magnitude without sacrificing ecological consistency and fundamental emergent phenomena. It also enables a full range of interactive control: artists and designers can directly steer the simulation through a painting interface, tune global ecological or climate parameters on the fly, and immediately observe the impact of their choices, making the system suitable for interactive authoring of virtual ecosystems, at a landscape scale of up to 8x8 km2 and 100 million trees (at update rates below 47 ms per simulated year). Much larger ecosystems (e.g., 24 × 24 km2 with 975 million trees) can still be simulated in about 413 ms per simulated year.

In the real world, humans can navigate through crowded spaces without collision because they are able to predict others’ intentions and avoidance strategies. However, navigating among autonomous virtual humans in Virtual Reality (VR) can be challenging due to the absence of anticipatory cues that communicate turning intention. In this paper, we study which body-part kinematic cues are relevant using both a human perceptual experiment in Virtual Reality (VR) and a Reinforcement Learning (RL) analysis sharing the same task formulation. First, we examine participants’ gaze behavior while predicting the avoidance direction of an approaching virtual pedestrian animated using full-body motion capture and gaze-tracking data from a real person. Second, we train RL agents on the same task and systematically ablate input features to quantify the contribution of each kinematic feature. Our gaze analysis shows that participants predominantly attend to the head and upper body, while lower-body fixations are associated with prediction errors. Our RL ablation identifies hip position as the dominant cue for accurate prediction, with head rotation providing a secondary but significant contribution to early decisions. Integrating both analyses, we identify a two-phase cue structure: an early orientation phase driven by head rotation, followed by a displacement phase driven by lateral hip movement. Our results show that realistic movement and gaze cues enable virtual humans to convey turning intentions, highlighting the need to incorporate these cues into simulation models. Moreover, the strong correlation between RL agents and perceptual results suggests that RL can help validate collision-avoidance models and support more natural human–agent interactions in virtual environments.

We present a reinforcement learning framework for crowd simulation that balances collision avoidance with navigation preferences guided by soft environmental constraints. Our approach integrates two complementary perception components: entity perception, which handles hard constraints imposed by physical obstacles, and implicit environmental perception, encoded as suitability maps that guide movement preferences due to soft constraints. To ensure robust generalization across complex scenes, we employ a modular, two-phase training strategy utilizing curriculum-based environmental templates. The proposed framework functions as an intuitive crowd authoring tool: artists can influence crowd behavior in real time by interactively editing suitability maps or dynamically adjusting perception weights, either globally or per-agent. This enables adaptive navigation behaviors, such as dynamic trajectory prioritization based on the environment, to emerge from local interactions. We validate our approach using both quantitative metrics and qualitative analysis, including a user study confirming that the resulting behaviors align with pedestrian traffic patterns observed in real-world settings. We further present an example of emergent social behavior through the formation and gradual evolution of desire paths. This work contributes to the field of crowd simulation by offering a robust, learning-based framework that supports heterogeneous navigation modeling and intuitive authoring for interactive applications.

We consider the problem of analyzing a movement trajectory across a terrain in order to obtain a human-readable description of the route. Our input consists of a trajectory (e.g., a GPS track) and a digital elevation model of the area where the trajectory takes place. The proposed method starts by computing a set of terrain-derived metrics, such as a landform classification, slope, and exposure. Based on these signals, the trajectory is segmented into meaningful parts, such as those going through ascending slopes, flat areas, summits, or ridges, and their descriptive characteristics (e.g., steep, gentle, exposed). Finally, using these elements, we apply a Large Language Model (LLM) to transform the data into a natural language description of the route. The method is controlled by a single parameter that adjusts the number of relevant items in the trajectory, allowing users to easily obtain descriptions with varying levels of detail.

Traditional navigation meshes split the terrain into convex walkable regions, which work well for nearly flat environments with clearly defined obstacles. However, in complex outdoor terrains with variable slopes and heights, these approaches fail to capture areas with consistent characteristics. Moreover, human preferences vary per individual: some may favour smooth paths with gentle slopes, while others prefer shorter routes even if steeper. We present a semantic navigation mesh that generates cells with coherent, low-variance values for chosen semantics, such as slope or vegetation density. Cells are constructed via a region-growing partitioning algorithm, optionally refined for compactness, and can incorporate multiple semantic dimensions simultaneously. This approach preserves terrain complexity without relying on a binary walkable/non-walkable classification. From this partition, we propose different alternatives for generating a navigation graph and perform semantically-aware pathfinding for agent-specific cost functions, which prevents the use of precomputed hierarchical paths. Our evaluation demonstrates that semantic meshes accelerate pathfinding on complex open terrains while producing trajectories close to those obtained by a brute-force search over the entire domain. This provides a flexible, generic framework that balances graph size, subdomain homogeneity, search efficiency, and path quality.

We present the Lunar Ribus Database, a global catalogue of lunar summits with topographic prominence ≥1 km derived from the highest-resolution global and regional digital elevation models currently available. Each summit in the catalogue is reported with summit and key col elevations relative to the global minimum, prominence, key col elevation, and geographic coordinates of both summit and key col. Prominence was computed using the divide-tree algorithm of Andrew Kirmse (“mountains”). Summits with prominence ≥1 km, called Ribus, provide a formal measure of morphometric independence. By linking each summit to its key col, the Lunar Ribus Database establishes a globally consistent hierarchy of kilometre-scale positive-relief landforms on the Moon. This framework enables terrain segmentation, analysis of impact- and volcanism-related edifices, and comparative prominence-based morphometry across planetary bodies.

We present TreeON, a novel neural-based framework for reconstructing detailed 3D tree point clouds from sparse top-down geodata, using only a single orthophoto and its corresponding Digital Surface Model (DSM). Our method introduces a new training supervision strategy that combines both geometric supervision and a differentiable shadow and silhouette loss to learn point cloud representations of trees without requiring species labels, procedural rules, detailed terrestrial reconstruction data, or ground laser scan data. To address the lack of ground truth data, we generate a synthetic dataset of point clouds from procedurally modeled trees and train our network on it. Quantitative and qualitative experiments demonstrate better reconstruction quality and coverage compared to existing methods, as well as strong generalization to real-world data, leading to visually appealing and structurally plausible tree point cloud representations that can be integrated into interactive digital 3D maps.

Digital terrains are central to realistic landscape depiction, yet authoring tools must balance perceptual realism with intuitive artistic control. We propose a compact vector-based representation that models terrain as nested iso-contours, inspired by geomorphology and cartography. Our method departs from traditional grid-based elevation models by generating contours through an inward Open Eden Growth simulation, followed by marching-triangles reconstruction into a Triangulated Irregular Network. This contour framework supports direct editing such as warping, slope modulation, and smoothing, while allowing reconstruction of a standard elevation map for downstream processing, including erosion and amplification. The approach enables the creation of diverse, realistic terrains from minimal user input and offers simple yet powerful control for designers.

Rule-breaking behaviors, such as jaywalking or skipping queues, are common in crowds but difficult to study in real-world settings due to limited control and observability. Virtual reality (VR) provides a controlled alternative, but its validity depends on whether VR elicits realistic rule-breaking behavior. We conducted a VR study with 65 participants navigating a virtual city with four scenarios differing in social norms: walking on grass, crossing outside a crosswalk, jaywalking at a red light, and skipping a line. In each scenario, the proportion of rule-breaking virtual characters was manipulated (0%, 25%, 50%). Participants’ movements and gaze were recorded to assess behavior and attention. Results showed higher rule-breaking as the number of violators increased, except in the low-stakes crossing scenario. Rule-breakers attended more to violating characters, whereas rule-followers focused on compliant ones. Participants cited efficiency, safety, and social norms as key factors guiding their decisions. Overall, VR reproduced natural patterns of social compliance and noncompliance, supporting its use for studying rule-breaking and applications in crowd simulation, urban design, safety training, and immersive media.

Performing everyday tasks requires both large-scale body movements driven by the arms, legs, and torso, and fine motor skills, particularly in the hands. However, existing reinforcement learning approaches often struggle to efficiently acquire these diverse motion skills from scratch, leading to slow convergence or suboptimal policies. We observed that the motor skills of distinct body parts exhibit a certain level of independence. This suggests the potential advantage of independently pretraining specific dexterous limbs prior to their integration in full-body motion tasks. Inspired by this, we present Part-wise Heterogeneous Agents (PHA), a cooperative multi-agent reinforcement learning approach where body parts are treated as independent agents, allowing for specialized skill acquisition and cooperative execution of complex full-body tasks. Furthermore, our method enables the pretraining of fine motor skills, such as gripping a bar or grabbing a climbing hold, before integrating them with other body parts for complex whole-body coordination, thus introducing part-wise Reusable Policy Priors. We tested our technique on challenging tasks such as rope climbing, rock bouldering and traversing a horizontal ladder. Our approach not only accelerates convergence, but also improves overall policy quality, achieving motion tasks that single-agent approaches struggle to solve. Our results also demonstrate adaptability, enabling Reusable Policy Priors to adjust their policies to successfully perform complex tasks in scenarios not seen during training.

Traditional navigation meshes are typically based on splitting the terrain into connected convex regions representing walkable cells. This works well for almost flat terrains where obstacles are clearly defined by walls or holes. When applied to complex outdoor environments with many changes in terrain height and slope, traditional approaches fail to correctly identify the walkable areas. Current navigation meshes require the user to specify the character’s maximum step size and slope, and then classify the environment as walkable or non-walkable, thus limiting the flexibility to adjust paths to the agents’ characteristics. Even if some terrain properties are then computed to add semantics to the navigation mesh, many cells could cover a wide range of values, as this information was ignored during its generation. In this paper, we present a novel approach to generate semantic navigation meshes, where the generated cells have a coherent and low-variance range of values for the chosen semantics (e.g., slope). Cell generation is performed with a semantic partitioning based on a region-growing algorithm. Our navigation mesh allows us to preserve the full complexity of the terrain without forcing a binary decision between walkable and non-walkable and provides useful semantics for the pathfinding algorithm.

Synthetic landscape generation is an active research area within Computer Graphics. Algorithms for terrain synthesis and ecosystem simulations often rely on simple descriptors such as slope, light accessibility, and drainage area. Typically, the results are assessed from a perceptual standpoint, focusing primarily on visual plausibility. Other fields, such as Geomorphology and Earth Sciences, have already proposed several analytical descriptors to measure various terrain properties. This work aims to bridge the gap between these disciplines and Computer Graphics. We provide a comprehensive review of commonly used terrain metrics that may be relevant for landscape synthesis, analysis, or simulations. Additionally, we compare the approaches used in Computer Graphics to see if these metrics, or similar ones, have already been introduced. Moreover, we report feedback from a preliminary study conducted with a group of artists to evaluate the potential applications of previously unused metrics. By implementing all these metrics, we enable performance comparisons. Together with the provided correlation matrix, this helps identify instances where a simpler and faster metric can serve as a proxy for a more computationally intensive one.

Digital 3D maps created from digital elevation models (DEMs) cannot properly capture trees due to the 2.5D nature of the DEMs. Leveraging publicly available DEMs and orthophotos as the only input data sources, we present a fully automatic pipeline that models landmark trees. We conducted two crowdsourced user studies to evaluate visual appeal and scene recognizability using two different levels of detail of tree representations generated using our pipeline. Users found highly detailed trees much more appealing, and also easier and more trustworthy to recognize, with open-ended responses revealing key factors like realism, coherence, and tree shape influencing their preferences. However, the ability to recognize a location seems to depend more on the surrounding environment than the representation of the landmark tree in focus. We discuss the implications of these results for digital 3D outdoor maps.

Human and animal presence in natural landscapes is initially revealed by the immediate impact of their locomotion, from footprints to crushed grass. In this work, we present an approach to model the effects of virtual characters on natural terrains, focusing on the impact of human locomotion. We introduce a lightweight solution to compute accurate foot placement on uneven ground and infer dynamic foot pressure from kinematic animation data and the mass of the character. A ground and vegetation model enables us to effectively simulate the local impact of locomotion on soft soils and plants over time, resulting in the formation of visible paths. As our results show, we can parameterize various soil materials and vegetation types validated with real-world data. Our method can be used to significantly increase the realism of populated natural landscapes and the sense of presence in virtual applications and games.

Synthetic terrains play a vital role in various applications, including entertainment, training, and simulation. This work focuses on rocky terrains akin to those found in alpine environments, which contain many complex features such as sharp ridges, loose blocks, or overhangs that are often inadequately represented by standard 2D elevation maps. We propose a novel method based on a simplified simulation of mechanical erosion processes commonly observed in high-altitude terrains, in particular the weathering due to freeze-thaw cycles. The ultimate objective is to generate plausible rocky geometry from existing 3D models, as well as account for the temporal evolution due to these weathering processes. Additionally, we have developed an artist-friendly tool integrated as an add-on into Blender.

The creation of truly believable simulated natural environments remains an unsolved problem in Computer Graphics. This is, in part, due to a lack of visual variety. In nature, apart from variation due to abiotic and biotic growth factors, a significant role is played by disturbance events, such as fires, windstorms, disease, and death and decay processes, which give rise to both standing dead trees (snags) and downed woody debris (logs). For instance, snags constitute on average 10% of unmanaged forests by basal area, and logs account for 2.5 times this quantity. While previous systems have incorporated individual elements of disturbance (e.g., forest fires) and decay (e.g., the formation of humus), there has been no unifying treatment, perhaps because of the challenge of matching simulation results with generated geometric models. In this paper, we present a framework that combines an ecosystem simulation, which explicitly incorporates disturbance events and decay processes, with a model realization process, which balances the uniqueness arising from life history with the need for instancing due to memory constraints. We tested our hypothesis concerning the visual impact of disturbance and decay with a two-alternative forced-choice experiment (n = 116). Our findings are that the presence of dead wood in various forms, as snags or logs, significantly improves the believability of natural scenes, while, surprisingly, general variation in the number of model instances, with up to 8 models per species, and a focus on disturbance events, does not.

Sant Quirze de Pedret is a Romanesque church located in Cercs (Catalonia, Spain) at the foothills of the Pyrenees. Its walls harbored one of the most important examples of mural paintings in Catalan Romanesque Art. However, in two different campaigns (in 1921 and 1937) the paintings were removed using the strappo technique and transferred to museums for safekeeping. This detachment protected the paintings from being sold in the art market, but at the price of breaking the integrity of the monument. Nowadays, the paintings are exhibited in the Museu Nacional d’Art de Catalunya - MNAC (Barcelona, Catalonia) and the Museu Diocesà i Comarcal de Solsona - MDCS (Solsona, Catalonia). Some fragments of the paintings are still on the walls of the church. In this work, we present the methodology to digitally reconstruct the church building at its different phases and group the dispersed paintings in a single virtual church, commissioned by the MDCS. We have combined 3D reconstruction (LIDAR and photogrammetric using portable artificial illumination) and modeling techniques (including texture transfer between different shapes) to recover the integrity of the monument in a single 3D virtual model. Furthermore, we have reconstructed the church building at different significant historical moments and placed actual paintings on its virtual walls, based on archaeological knowledge. This set of 3D models allows experts and visitors to better understand the monument as a whole, the relations between the different paintings, and its evolution over time.

High-end Terrestrial Lidar Scanners are often equipped with RGB cameras that are used to colorize the point samples. Some of these scanners produce panoramic HDR images by encompassing the information of multiple pictures with different exposures. Unfortunately, exported RGB color values are not in an absolute color space, and thus point samples with similar reflectivity values might exhibit strong color differences depending on the scan the sample comes from. These color differences produce severe visual artifacts if, as usual, multiple point clouds colorized independently are combined into a single point cloud. In this paper we propose an automatic algorithm to minimize color differences among a collection of registered scans. The basic idea is to find correspondences between pairs of scans, i.e. surface patches that have been captured by both scans. If the patches meet certain requirements, their colors should match in both scans. We build a graph from such pair-wise correspondences, and solve for the gain compensation factors that better uniformize color across scans. The resulting panoramas can be used to colorize the point clouds consistently. We discuss the characterization of good candidate matches, and how to find such correspondences directly on the panorama images instead of in 3D space. We have tested this approach to uniformize color across scans acquired with a Leica RTC360 scanner, with very good results.

Glaciers are some of the most visually arresting and scenic elements of cold regions and high mountain landscapes. Although snow-covered terrains have previously received attention in computer graphics, simulating the temporal evolution of glaciers as well as modeling their wide range of features has never been addressed. In this paper, we combine a Shallow Ice Approximation simulation with a procedural amplification process to author high-resolution realistic glaciers. Our multiresolution method allows the interactive simulation of the formation and the evolution of glaciers over hundreds of years. The user can easily modify the environment variables, such as the average temperature or precipitation rate, to control the glacier growth, or directly use brushes to sculpt the ice or bedrock with interactive feedback. Mesoscale and smallscale landforms that are not captured by the glacier simulation, such as crevasses, moraines, seracs, ogives, or icefalls are synthesized using procedural rules inspired by observations in glaciology and according to the physical parameters derived from the simulation. Our method lends itself to seamless integration into production pipelines to decorate reliefs with glaciers and realistic ice features.

We present an interactive aeolian simulation to author hot desert scenery. Wind is an important erosion agent in deserts which, despite its importance, has been neglected in computer graphics. Our framework overcomes this and allows generating a variety of sand dunes, including barchans, longitudinal and anchored dunes, and simulates abrasion which erodes bedrock and sculpts complex landforms. Given an input time varying high altitude wind field, we compute the wind field at the surface of the terrain according to the relief, and simulate the transport of sand blown by the wind. The user can interactively model complex desert landscapes, and control their evolution throughout time either by using a variety of interactive brushes or by prescribing events along a user-defined time-line.

Mountainous digital terrains are an important element of many virtual environments and find application in games, film, simulation and training. Unfortunately, while existing synthesis methods produce locally plausible results they often fail to respect global structure. This is exacerbated by a dearth of automated metrics for assessing terrain properties at a macro level. We address these issues by building on techniques from orometry, a field that involves the measurement of mountains and other relief features. First, we construct a sparse metric computed on the peaks and saddles of a mountain range and show that, when used for classification, this is capable of robustly distinguishing between different mountain ranges. Second, we present a synthesis method that takes a coarse elevation map as input and builds a graph of peaks and saddles respecting a given orometric distribution. This is then expanded into a fully continuous elevation function by deriving a consistent river network and shaping the valley slopes. In terms of authoring, users provide various control maps and are also able to edit, reposition, insert and remove terrain features all while retaining the characteristics of a selected mountain range. The result is a terrain analysis and synthesis method that considers and incorporates orometric properties, and is, on the basis of our user study, more visually plausible than existing terrain generation methods.

The automatic generation of realistic vegetation closely reproducing the appearance of specific plant species is still a challenging topic in computer graphics. In this paper, we present a new approach to generate new tree models from a small collection of frontal RGBA images of trees. The new models are represented either as single billboards (suitable for still image generation in areas such as architecture rendering) or as billboard clouds (providing parallax effects in interactive applications). Key ingredients of our method include the synthesis of new contours through convex combinations of exemplar countours, the automatic segmentation into crown/trunk classes and the transfer of RGBA colour from the exemplar images to the synthetic target. We also describe a fully automatic approach to convert a single tree image into a billboard cloud by extracting superpixels and distributing them inside a silhouette‐defined 3D volume. Our algorithm allows for the automatic generation of an arbitrary number of tree variations from minimal input, and thus provides a fast solution to add vegetation variety in outdoor scenes.

Despite recent advances in surveying techniques, publicly available Digital Elevation Models (DEMs) of terrains are low-resolution except for selected places on Earth. In this paper we present a new method to turn low-resolution DEMs into plausible and faithful high-resolution terrains. Unlike other approaches for terrain synthesis/amplification (fractal noise, hydraulic and thermal erosion, multi-resolution dictionaries), we benefit from high-resolution aerial images to produce highly-detailed DEMs mimicking the features of the real terrain. We explore different architectures for Fully Convolutional Neural Networks to learn upsampling patterns for DEMs from detailed training sets (high-resolution DEMs and orthophotos), yielding up to one order of magnitude more resolution. Our comparative results show that our method outperforms competing data amplification approaches in terms of elevation accuracy and terrain plausibility.

During the last years, we have witnessed significant improvements in digital terrain modeling, mainly through photogrammetric techniques based on satellite and aerial photography, as well as laser scanning. These techniques allow the creation of Digital Elevation Models (DEM) and Digital Surface Models (DSM) that can be streamed over the network and explored through virtual globe applications like Google Earth or NASA WorldWind. The resolution of these 3D scenes has improved noticeably in the last years, reaching in some urban areas resolutions up to 1m or less for DEM and buildings, and less than 10 cm per pixel in the associated aerial imagery. However, in rural, forest or mountainous areas, the typical resolution for elevation datasets ranges between 5 and 30 meters, and typical resolution of corresponding aerial photographs ranges between 25 cm to 1 m. This current level of detail is only sufficient for aerial points of view, but as the viewpoint approaches the surface the terrain loses its realistic appearance.

One approach to augment the detail on top of currently available datasets is adding synthetic details in a plausible manner, i.e. including elements that match the features perceived in the aerial view. By combining the real dataset with the instancing of models on the terrain and other procedural detail techniques, the effective resolution can potentially become arbitrary. There are several applications that do not need an exact reproduction of the real elements but would greatly benefit from plausibly enhanced terrain models: videogames and entertainment applications, visual impact assessment (e.g. how a new ski resort would look), virtual tourism, simulations, etc.

In this thesis we propose new methods and tools to help the reconstruction and synthesis of high-resolution terrain scenes from currently available data sources, in order to achieve realistically looking ground-level views. In particular, we decided to focus on rural scenarios, mountains and forest areas. Our main goal is the combination of plausible synthetic elements and procedural detail with publicly available real data to create detailed 3D scenes from existing locations. Our research has focused on the following contributions: - An efficient pipeline for aerial imagery segmentation - Plausible terrain enhancement from high-resolution examples - Super-resolution of DEM by transferring details from the aerial photograph - Synthesis of arbitrary tree picture variations from a reduced set of photographs - Reconstruction of 3D tree models from a single image - A compact and efficient tree representation for real-time rendering of forest landscapes

Structure-from-motion along with multi-view stereo techniques jointly allow for the inexpensive scanning of 3D objects (e.g. buildings) using just a collection of images taken from commodity cameras. Despite major advances in these fields, a major limitation of dense reconstruction algorithms is that correct depth/normal values are not recovered on specular surfaces (e.g. windows) and parts lacking image features (e.g. flat, textureless parts of the facade). Since these reflective properties are inherent to the surface being acquired, images from different viewpoints hardly contribute to solve this problem. In this paper we present a simple method for detecting, classifying and filling non-valid data regions in depth maps produced by dense stereo algorithms. Triangles meshes reconstructed from our repaired depth maps exhibit much higher quality than those produced by state-of-the-art reconstruction algorithms like Screened Poisson-based techniques.

The cost-effective generation of realistic vegetation is still a challenging topic in computer graphics. The simplest representation of a tree consists of a single texture-mapped billboard. Although a tree billboard does not support top views, this is the most common representation for still image generation in areas such as architecture rendering. In this paper we present a new approach to generate new tree models from a small collection of RGBA images of trees. Key ingredients of our method are the representation of the tree contour space with a small set of basis vectors, the automatic crown/trunk segmentation, and the continuous transfer of RGBA color from the exemplar images to the synthetic target. Our algorithm allows the efficient generation of an arbitrary number of tree variations and thus provides a fast solution to add variety among trees in outdoor scenes.

The visual enrichment of digital terrain models with plausible synthetic detail requires the segmentation of aerial images into a suitable collection of categories. In this paper we present a complete pipeline for segmenting high-resolution aerial images into a user-defined set of categories distinguishing e.g. terrain, sand, snow, water, and different types of vegetation. This segmentation-for-synthesis problem implies that per-pixel categories must be established according to the algorithms chosen for rendering the synthetic detail. This precludes the definition of a universal set of labels and hinders the construction of large training sets. Since artists might choose to add new categories on the fly, the whole pipeline must be robust against unbalanced datasets, and fast on both training and inference. Under these constraints, we analyze the contribution of common per-pixel descriptors, and compare the performance of state-of-the-art supervised learning algorithms. We report the findings of two user studies. The first one was conducted to analyze human accuracy when manually labeling aerial images. The second user study compares detailed terrains built using different segmentation strategies, including official land cover maps. These studies demonstrate that our approach can be used to turn digital elevation models into fully-featured, detailed terrains with minimal authoring efforts.

We present an efficient method for generating coherent multi-layer landscapes. We use a dictionary built from exemplars to synthesize high-resolution fully featured terrains from input low-resolution elevation data. Our example-based method consists in analyzing real-world terrain examples and learning the procedural rules directly from these inputs. We take into account not only the elevation of the terrain, but also additional layers such as the slope, orientation, drainage area, the density and distribution of vegetation, and the soil type. By increasing the variety of terrain exemplars, our method allows the user to synthesize and control different types of landscapes and biomes, such as temperate or rain forests, arid deserts and mountains.

State-of-the-art approaches for tree reconstruction either put limiting constraints on the input side (requiring multiple photographs, a scanned point cloud or intensive user input) or provide a representation only suitable for front views of the tree. In this paper we present a complete pipeline for synthesizing and rendering detailed trees from a single photograph with minimal user effort. Since the overall shape and appearance of each tree is recovered from a single photograph of the tree crown, artists can benefit from georeferenced images to populate landscapes with native tree species. A key element of our approach is a compact representation of dense tree crowns through a radial distance map. Our first contribution is an automatic algorithm for generating such representations from a single exemplar image of a tree. We create a rough estimate of the crown shape by solving a thin-plate energy minimization problem, and then add detail through a simplified shape-from-shading approach. The use of seamless texture synthesis results in an image-based representation that can be rendered from arbitrary view directions at different levels of detail. Distant trees benefit from an output-sensitive algorithm inspired on relief mapping. For close-up trees we use a billboard cloud where leaflets are distributed inside the crown shape through a space colonization algorithm. In both cases our representation ensures efficient preservation of the crown shape. Major benefits of our approach include: it recovers the overall shape from a single tree image, involves no tree modeling knowledge and minimal authoring effort, and the associated image-based representation is easy to compress and thus suitable for network streaming.

The use of virtual prototypes and digital models containing thousands of individual objects is commonplace in complex industrial applications like the cooperative design of huge ships. Designers are interested in selecting and editing specific sets of objects during the interactive inspection sessions. This is however not supported by standard visualization systems for huge models. In this paper we discuss in detail the concept of rendering front in multiresolution trees, their properties and the algorithms that construct the hierarchy and efficiently render it, applied to very complex CAD models, so that the model structure and the identities of objects are preserved. We also propose an algorithm for the interactive inspection of huge models which uses a rendering budget and supports selection of individual objects and sets of objects, displacement of the selected objects and real-time collision detection during these displacements. Our solution–based on the analysis of several existing view-dependent visualization schemes–uses a Hybrid Multiresolution Tree that mixes layers of exact geometry, simplified models and impostors, together with a time-critical, view-dependent algorithm and a Constrained Front. The algorithm has been successfully tested in real industrial environments; the models involved are presented and discussed in the paper.

We discuss bi-harmonic fields which approximate signed distance fields. We conclude that the bi-harmonic field approximation can be a powerful tool for mesh completion in general and complex cases. We present an adaptive, multigrid algorithm to extrapolate signed distance fields. By defining a volume mask in a closed region bounding the area that must be repaired, the algorithm computes a signed distance field in well-defined regions and uses it as an over-determined boundary condition constraint for the biharmonic field computation in the remaining regions. The algorithm operates locally, within an expanded bounding box of each hole, and therefore scales well with the number of holes in a single, complex model. We discuss this approximation in practical examples in the case of triangular meshes resulting from laser scan acquisitions which require massive hole repair. We conclude that the proposed algorithm is robust and general, and is able to deal with complex topological cases.

In this paper we propose a photon mappingbased technique for the efficient rendering of urban landscapes. Unlike traditional photon mapping approaches, we accumulate the photon energy into a collection of persistent 2D photon buffers encoding the incoming radiance for a superset of the surfaces contributing to the current image. We define an implicit parameterization to map surface points onto photon buffer locations. This is achieved through a cylindrical projection for the building blocks plus an orthogonal projection for the terrain. An adaptive scheme is used to adapt the resolution of the photon buffers to the viewing conditions. Our customized photon mapping algorithm combines multiple acceleration strategies to provide efficient rendering during walkthroughs and flythroughs with minimal temporal artifacts. To the best of our knowledge, the algorithm we present in this paper is the first one to address the problem of interactive global illumination for large urban landscapes.