444 research outputs found
MD-Splatting: Learning Metric Deformation from 4D Gaussians in Highly Deformable Scenes
Accurate 3D tracking in highly deformable scenes with occlusions and shadows
can facilitate new applications in robotics, augmented reality, and generative
AI. However, tracking under these conditions is extremely challenging due to
the ambiguity that arises with large deformations, shadows, and occlusions. We
introduce MD-Splatting, an approach for simultaneous 3D tracking and novel view
synthesis, using video captures of a dynamic scene from various camera poses.
MD-Splatting builds on recent advances in Gaussian splatting, a method that
learns the properties of a large number of Gaussians for state-of-the-art and
fast novel view synthesis. MD-Splatting learns a deformation function to
project a set of Gaussians with non-metric, thus canonical, properties into
metric space. The deformation function uses a neural-voxel encoding and a
multilayer perceptron (MLP) to infer Gaussian position, rotation, and a shadow
scalar. We enforce physics-inspired regularization terms based on local
rigidity, conservation of momentum, and isometry, which leads to trajectories
with smaller trajectory errors. MD-Splatting achieves high-quality 3D tracking
on highly deformable scenes with shadows and occlusions. Compared to
state-of-the-art, we improve 3D tracking by an average of 23.9 %, while
simultaneously achieving high-quality novel view synthesis. With sufficient
texture such as in scene 6, MD-Splatting achieves a median tracking error of
3.39 mm on a cloth of 1 x 1 meters in size. Project website:
https://md-splatting.github.io/
Transition Contour Synthesis with Dynamic Patch Transitions
In this article, we present a novel approach for modulating the shape of transitions between terrain materials to produce detailed and varied contours where blend resolution is limited. Whereas texture splatting and blend mapping add detail to transitions at the texel level, our approach addresses the broader shape of the transition by introducing intermittency and irregularity. Our results have proven that enriched detail of the blend contour can be achieved with a performance competitive to existing approaches without additional texture, geometry resources, or asset preprocessing. We achieve this by compositing blend masks on-the-fly with the subdivision of texture space into differently sized patches to produce irregular contours from minimal artistic input. Our approach is of particular importance for applications where GPU resources or artistic input is limited or impractical
Point'n Move: Interactive Scene Object Manipulation on Gaussian Splatting Radiance Fields
We propose Point'n Move, a method that achieves interactive scene object
manipulation with exposed region inpainting. Interactivity here further comes
from intuitive object selection and real-time editing. To achieve this, we
adopt Gaussian Splatting Radiance Field as the scene representation and fully
leverage its explicit nature and speed advantage. Its explicit representation
formulation allows us to devise a 2D prompt points to 3D mask dual-stage
self-prompting segmentation algorithm, perform mask refinement and merging,
minimize change as well as provide good initialization for scene inpainting and
perform editing in real-time without per-editing training, all leads to
superior quality and performance. We test our method by performing editing on
both forward-facing and 360 scenes. We also compare our method against existing
scene object removal methods, showing superior quality despite being more
capable and having a speed advantage
Performance and quality analysis of convolution-based volume illumination
Convolution-based techniques for volume rendering are among the fastest in the on-the-fly volumetric illumination
category. Such methods, however, are still considerably slower than conventional local illumination techniques.
In this paper we describe how to adapt two commonly used strategies for reducing aliasing artifacts, namely
pre-integration and supersampling, to such techniques. These strategies can help reduce the sampling rate of the
lighting information (thus the number of convolutions), bringing considerable performance benefits. We present a
comparative analysis of their effectiveness in offering performance improvements. We also analyze the (negligible)
differences they introduce when comparing their output to the reference method.
These strategies can be highly beneficial in setups where direct volume rendering of continuously streaming data is
desired and continuous recomputation of full lighting information is too expensive, or where memory constraints
make it preferable not to keep additional precomputed volumetric data in memory. In such situations these strategies
make single pass, convolution-based volumetric illumination models viable for a broader range of applications,
and this paper provides practical guidelines for using and tuning such strategies to specific use cases
Stream programming framework for global ilumination techniques using a GPU
Los procesadores de streams están comenzando a ser una alternativa accesible para implementar técnicas de rendering asistidas por hardware que habitualmente estaban relegadas al uso offline.
Nosotros elaboramos un marco de trabajo para procesamiento de streams basado en los conceptos del modelo de Stream Programming, seleccionamos el algoritmo de Photon Mapping y una GPU (Graphics Processing Unit) Nvidia para una implementación de un caso de prueba. Definimos un conjunto de clases en C++ para encapsular los componentes (kernels y streams) de este nuevo paradigma, usando OpenGL y el lenguaje Cg. Nuestra aplicación combina el método de Photon Mapping y una estructura de aceleración BVH (Bounding Volumes Hierarchy) en un pipeline de renderizado basado casi completamente en la GPU. Finalmente, evaluamos su desempeño usando un modelo de caja de Cornell.Stream processors are becoming an affordable alternative to implement hardware assisted rendering techniques which were usually relegated to offline usage. We built a stream processing framework based on the Stream Programming Model concepts, selected the Photon Mapping algorithm and an NVIDIA GPU (Graphics Processing Unit) as a test case implementation of a Global Illumination technique. We defined a set of C++ classes to encapsulate the components (kernels and streams) of this new paradigm, using OpenGL and Cg language. Our application combines the Photon Splatting method and the BVH (Bounding Volumes Hierarchy) acceleration structure into a rendering pipeline relying almost entirely on the GPU. Finally, we evaluated its performance using a Cornell Box model.V Workshop de Computación Gráfica, Imágenes Y VisualizaciónRed de Universidades con Carreras en Informática (RedUNCI
Drivable 3D Gaussian Avatars
We present Drivable 3D Gaussian Avatars (D3GA), the first 3D controllable
model for human bodies rendered with Gaussian splats. Current photorealistic
drivable avatars require either accurate 3D registrations during training,
dense input images during testing, or both. The ones based on neural radiance
fields also tend to be prohibitively slow for telepresence applications. This
work uses the recently presented 3D Gaussian Splatting (3DGS) technique to
render realistic humans at real-time framerates, using dense calibrated
multi-view videos as input. To deform those primitives, we depart from the
commonly used point deformation method of linear blend skinning (LBS) and use a
classic volumetric deformation method: cage deformations. Given their smaller
size, we drive these deformations with joint angles and keypoints, which are
more suitable for communication applications. Our experiments on nine subjects
with varied body shapes, clothes, and motions obtain higher-quality results
than state-of-the-art methods when using the same training and test data.Comment: Website: https://zielon.github.io/d3ga
Relightable 3D Gaussian: Real-time Point Cloud Relighting with BRDF Decomposition and Ray Tracing
We present a novel differentiable point-based rendering framework for
material and lighting decomposition from multi-view images, enabling editing,
ray-tracing, and real-time relighting of the 3D point cloud. Specifically, a 3D
scene is represented as a set of relightable 3D Gaussian points, where each
point is additionally associated with a normal direction, BRDF parameters, and
incident lights from different directions. To achieve robust lighting
estimation, we further divide incident lights of each point into global and
local components, as well as view-dependent visibilities. The 3D scene is
optimized through the 3D Gaussian Splatting technique while BRDF and lighting
are decomposed by physically-based differentiable rendering. Moreover, we
introduce an innovative point-based ray-tracing approach based on the bounding
volume hierarchy for efficient visibility baking, enabling real-time rendering
and relighting of 3D Gaussian points with accurate shadow effects. Extensive
experiments demonstrate improved BRDF estimation and novel view rendering
results compared to state-of-the-art material estimation approaches. Our
framework showcases the potential to revolutionize the mesh-based graphics
pipeline with a relightable, traceable, and editable rendering pipeline solely
based on point cloud. Project
page:https://nju-3dv.github.io/projects/Relightable3DGaussian/
Efficient Linear Programming for Dense CRFs
The fully connected conditional random field (CRF) with Gaussian pairwise
potentials has proven popular and effective for multi-class semantic
segmentation. While the energy of a dense CRF can be minimized accurately using
a linear programming (LP) relaxation, the state-of-the-art algorithm is too
slow to be useful in practice. To alleviate this deficiency, we introduce an
efficient LP minimization algorithm for dense CRFs. To this end, we develop a
proximal minimization framework, where the dual of each proximal problem is
optimized via block coordinate descent. We show that each block of variables
can be efficiently optimized. Specifically, for one block, the problem
decomposes into significantly smaller subproblems, each of which is defined
over a single pixel. For the other block, the problem is optimized via
conditional gradient descent. This has two advantages: 1) the conditional
gradient can be computed in a time linear in the number of pixels and labels;
and 2) the optimal step size can be computed analytically. Our experiments on
standard datasets provide compelling evidence that our approach outperforms all
existing baselines including the previous LP based approach for dense CRFs.Comment: 24 pages, 10 figures and 4 table
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