3,060 research outputs found
A survey of real-time crowd rendering
In this survey we review, classify and compare existing approaches for real-time crowd rendering. We first overview character animation techniques, as they are highly tied to crowd rendering performance, and then we analyze the state of the art in crowd rendering. We discuss different representations for level-of-detail (LoD) rendering of animated characters, including polygon-based, point-based, and image-based techniques, and review different criteria for runtime LoD selection. Besides LoD approaches, we review classic acceleration schemes, such as frustum culling and occlusion culling, and describe how they can be adapted to handle crowds of animated characters. We also discuss specific acceleration techniques for crowd rendering, such as primitive pseudo-instancing, palette skinning, and dynamic key-pose caching, which benefit from current graphics hardware. We also address other factors affecting performance and realism of crowds such as lighting, shadowing, clothing and variability. Finally we provide an exhaustive comparison of the most relevant approaches in the field.Peer ReviewedPostprint (author's final draft
Output-Sensitive Rendering of Detailed Animated Characters for Crowd Simulation
High-quality, detailed animated characters are often represented as textured
polygonal meshes. The problem with this technique is the high cost
that involves rendering and animating each one of these characters. This
problem has become a major limiting factor in crowd simulation. Since we
want to render a huge number of characters in real-time, the purpose of
this thesis is therefore to study the current existing approaches in crowd
rendering to derive a novel approach.
The main limitations we have found when using impostors are (1) the
big amount of memory needed to store them, which also has to be sent
to the graphics card, (2) the lack of visual quality in close-up views, and
(3) some visibility problems. As we wanted to overcome these limitations,
and improve performance results, the found conclusions lead us to present
a new representation for 3D animated characters using relief mapping, thus
supporting an output-sensitive rendering.
The basic idea of our approach is to encode each character through a
small collection of textured boxes storing color and depth values. At runtime,
each box is animated according to the rigid transformation of its associated
bone in the animated skeleton. A fragment shader is used to recover
the original geometry using an adapted version of relief mapping. Unlike
competing output-sensitive approaches, our compact representation is able
to recover high-frequency surface details and reproduces view-motion parallax
e ects. Furthermore, the proposed approach ensures correct visibility
among di erent animated parts, and it does not require us to prede ne the
animation sequences nor to select a subset of discrete views. Finally, a user
study demonstrates that our approach allows for a large number of simulated
agents with negligible visual artifacts
A Global Human Settlement Layer from optical high resolution imagery - Concept and first results
A general framework for processing of high and very-high resolution imagery for creating a Global Human Settlement Layer (GHSL) is presented together with a discussion on the results of the first operational test of the production workflow. The test involved the mapping of 24.3 millions of square kilometres of the Earth surface spread over four continents, corresponding to an estimated population of 1.3 billion of people in 2010. The resolution of the input image data ranges from 0.5 to 10 meters, collected by a heterogeneous set of platforms including satellite SPOT (2 and 5), CBERS-2B, RapidEye (2 and 4), WorldView (1 and 2), GeoEye-1, QuickBird-2, Ikonos-2, and airborne sensors. Several imaging modes were tested including panchromatic, multispectral and pan-sharpened images. A new fully automatic image information extraction, generalization and mosaic workflow is presented that is based on multiscale textural and morphological image features extraction. New image feature compression and optimization are introduced, together with new learning and classification techniques allowing for the processing of HR/VHR image data using low-resolution thematic layers as reference. A new systematic approach for quality control and validation allowing global spatial and thematic consistency checking is proposed and applied. The quality of the results are discussed by sensor, by band, by resolution, and eco-regions. Critical points, lessons learned and next steps are highlighted.JRC.G.2-Global security and crisis managemen
Learning Gradient Fields for Scalable and Generalizable Irregular Packing
The packing problem, also known as cutting or nesting, has diverse
applications in logistics, manufacturing, layout design, and atlas generation.
It involves arranging irregularly shaped pieces to minimize waste while
avoiding overlap. Recent advances in machine learning, particularly
reinforcement learning, have shown promise in addressing the packing problem.
In this work, we delve deeper into a novel machine learning-based approach that
formulates the packing problem as conditional generative modeling. To tackle
the challenges of irregular packing, including object validity constraints and
collision avoidance, our method employs the score-based diffusion model to
learn a series of gradient fields. These gradient fields encode the
correlations between constraint satisfaction and the spatial relationships of
polygons, learned from teacher examples. During the testing phase, packing
solutions are generated using a coarse-to-fine refinement mechanism guided by
the learned gradient fields. To enhance packing feasibility and optimality, we
introduce two key architectural designs: multi-scale feature extraction and
coarse-to-fine relation extraction. We conduct experiments on two typical
industrial packing domains, considering translations only. Empirically, our
approach demonstrates spatial utilization rates comparable to, or even
surpassing, those achieved by the teacher algorithm responsible for training
data generation. Additionally, it exhibits some level of generalization to
shape variations. We are hopeful that this method could pave the way for new
possibilities in solving the packing problem
Automated 3D model generation for urban environments [online]
Abstract
In this thesis, we present a fast approach to automated
generation of textured 3D city models with both high details at
ground level and complete coverage for birds-eye view.
A ground-based facade model is acquired by driving a vehicle
equipped with two 2D laser scanners and a digital camera under
normal traffic conditions on public roads. One scanner is
mounted horizontally and is used to determine the approximate
component of relative motion along the movement of the
acquisition vehicle via scan matching; the obtained relative
motion estimates are concatenated to form an initial path.
Assuming that features such as buildings are visible from both
ground-based and airborne view, this initial path is globally
corrected by Monte-Carlo Localization techniques using an aerial
photograph or a Digital Surface Model as a global map. The
second scanner is mounted vertically and is used to capture the
3D shape of the building facades. Applying a series of automated
processing steps, a texture-mapped 3D facade model is
reconstructed from the vertical laser scans and the camera
images. In order to obtain an airborne model containing the roof
and terrain shape complementary to the facade model, a Digital
Surface Model is created from airborne laser scans, then
triangulated, and finally texturemapped with aerial imagery.
Finally, the facade model and the airborne model are fused
to one single model usable for both walk- and fly-thrus. The
developed algorithms are evaluated on a large data set acquired
in downtown Berkeley, and the results are shown and discussed
Quantitative evaluation of peptide analogue distribution in mouse tissue using 3D computer modelling
- …