4 citations · 12 across the 15 of their papers we have counts for
18 papers
ADELE - Adaptive Delaunay Grids for High-Fidelity Mesh-Native Reconstruction
Johannes Weidenfeller, Shaofei Wang, Philipp Fürnstahl +1
Meshes remain the most practical representation for geometry reasoning and integration into graphics pipelines, yet existing reconstruction methods struggle to produce high-quality…
MV-dVRK: A Multi-Viewpoint Benchmark for Spatial Surgical Perception
Guido Caccianiga, Sergey Prokudin, Yutong Chen +9
Large-scale training and refined optimization techniques have greatly improved sparse multi-view 3D reconstruction. Despite their relevance to surgery, such methods have never befo…
TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views
Skanda Koppula, Frano Rajic, Abdullah Faiz Ur Rahman +9
Multi-camera systems are increasingly practical for robotics, AR/VR, and autonomous driving because complementary views reduce depth ambiguity and preserve visibility under occlusi…
GrowFields: Compositional 4D Neural Fields for Topology-Changing Plant Growth
Joaquin Gajardo, Michele Volpi, Marko Mihajlovic +3
Quantifying plant growth dynamics from sparse longitudinal 3D observations is fundamental for agriculture and plant sciences. Yet, plants pose unique challenges: they undergo intri…
Multi-View 3D Point Tracking
Frano Rajič, Haofei Xu, Marko Mihajlovic +7
We introduce the first data-driven multi-view 3D point tracker, designed to track arbitrary points in dynamic scenes using multiple camera views. Unlike existing monocular trackers…
Spline Deformation Field
Mingyang Song, Yang Zhang, Marko Mihajlovic +3
Trajectory modeling of dense points usually employs implicit deformation fields, represented as neural networks that map coordinates to relate canonical spatial positions to tempor…
Enhancing Orthopedic Surgical Training With Interactive Photorealistic 3D Visualization
Roni Lekar, Tatiana Gerth, Sergey Prokudin +7
Surgical training integrates several years of didactic learning, simulation, mentorship, and hands-on experience. Challenges include stress, technical demands, and new technologies…
DNF-Avatar: Distilling Neural Fields for Real-time Animatable Avatar Relighting
Zeren Jiang, Shaofei Wang, Siyu Tang
Creating relightable and animatable human avatars from monocular videos is a rising research topic with a range of applications, e.g. virtual reality, sports, and video games. Prev…
Relightable Full-Body Gaussian Codec Avatars
Shaofei Wang, Tomas Simon, Igor Santesteban +15
We propose Relightable Full-Body Gaussian Codec Avatars, a new approach for modeling relightable full-body avatars with fine-grained details including face and hands. The unique ch…
SplatFormer: Point Transformer for Robust 3D Gaussian Splatting
Yutong Chen, Marko Mihajlovic, Xiyi Chen +3
3D Gaussian Splatting (3DGS) has recently transformed photorealistic reconstruction, achieving high visual fidelity and real-time performance. However, rendering quality significan…
RISE-SDF: a Relightable Information-Shared Signed Distance Field for Glossy Object Inverse Rendering
Deheng Zhang, Jingyu Wang, Shaofei Wang +4
In this paper, we propose a novel end-to-end relightable neural inverse rendering system that achieves high-quality reconstruction of geometry and material properties, thus enablin…
SplatFields: Neural Gaussian Splats for Sparse 3D and 4D Reconstruction
Marko Mihajlovic, Sergey Prokudin, Siyu Tang +4
Digitizing 3D static scenes and 4D dynamic events from multi-view images has long been a challenge in computer vision and graphics. Recently, 3D Gaussian Splatting (3DGS) has emerg…
LaRa: Efficient Large-Baseline Radiance Fields
Anpei Chen, Haofei Xu, Stefano Esposito +2
Radiance field methods have achieved photorealistic novel view synthesis and geometry reconstruction. But they are mostly applied in per-scene optimization or small-baseline settin…
Degrees of Freedom Matter: Inferring Dynamics from Point Trajectories
Yan Zhang, Sergey Prokudin, Marko Mihajlovic +2
Understanding the dynamics of generic 3D scenes is fundamentally challenging in computer vision, essential in enhancing applications related to scene reconstruction, motion trackin…
Morphable Diffusion: 3D-Consistent Diffusion for Single-image Avatar Creation
Xiyi Chen, Marko Mihajlovic, Shaofei Wang +2
Recent advances in generative diffusion models have enabled the previously unfeasible capability of generating 3D assets from a single input image or a text prompt. In this work, w…
3DGS-Avatar: Animatable Avatars via Deformable 3D Gaussian Splatting
Zhiyin Qian, Shaofei Wang, Marko Mihajlovic +2
We introduce an approach that creates animatable human avatars from monocular videos using 3D Gaussian Splatting (3DGS). Existing methods based on neural radiance fields (NeRFs) ac…
IntrinsicAvatar: Physically Based Inverse Rendering of Dynamic Humans from Monocular Videos via Explicit Ray Tracing
Shaofei Wang, Božidar Antić, Andreas Geiger +1
We present IntrinsicAvatar, a novel approach to recovering the intrinsic properties of clothed human avatars including geometry, albedo, material, and environment lighting from onl…
ResFields: Residual Neural Fields for Spatiotemporal Signals
Marko Mihajlovic, Sergey Prokudin, Marc Pollefeys +1
Neural fields, a category of neural networks trained to represent high-frequency signals, have gained significant attention in recent years due to their impressive performance in m…