3D Vision

Object-centric occupancy completion boosts 3D object detection accuracy by using temporal information from long sequences to precisely reconstruct object shapes, particularly for incomplete or distant…

Dynamic NLOS imaging gets a speed boost! New ST-Mamba method leverages temporal consistency across frames for high-resolution video reconstruction, overcoming speed limitations of traditional methods.

Boosting 3D human pose estimation: Biomechanical Pose Generator and Binary Depth Coordinates enhance accuracy and scalability.

TFS-NeRF: A template-free neural radiance field efficiently reconstructs semantically separable 3D geometries of dynamic scenes featuring multiple interacting entities from sparse RGB videos.

TeT-Splatting: a novel 3D representation enabling fast convergence, real-time rendering, and precise mesh extraction for high-fidelity 3D generation.

Low-rank block trifocal tensor unlocks accurate, efficient camera pose synchronization.

This research introduces a template-free articulated Gaussian splatting method for real-time dynamic view synthesis, automatically discovering object skeletons from videos to enable reposing.

APCT: a novel architecture enhances 3D point cloud recognition by using an adversarial feature erasing mechanism to improve global structure capture and robustness against real-world corruptions.

Real-time rendering of objects with subsurface scattering effects is now possible with SSS-GS, a novel method combining explicit surface geometry and implicit subsurface scattering for high-quality no…

STONE: A novel submodular optimization framework drastically cuts 3D object detection training costs by cleverly selecting the most informative LiDAR point cloud data for labeling, achieving state-of-…

SplitNeRF: One-hour training on a single GPU yields state-of-the-art scene geometry, lighting, and material property estimation!

SFCNet, a novel spherical frustum sparse convolution network, tackles LiDAR point cloud semantic segmentation by eliminating quantized information loss, leading to superior performance, especially for…

SpelsNet, a novel neural architecture, achieves accurate 3D point cloud segmentation into surface primitives by incorporating B-Rep graph structure supervision, leading to topologically consistent res…

Spec-Gaussian enhances 3D Gaussian splatting by using anisotropic spherical Gaussians for view-dependent appearance modeling, achieving superior real-time rendering of scenes with specular and anisotr…

SparseAGS: High-fidelity 3D reconstruction & camera pose estimation from sparse views via generative synthesis.

SfPUEL: A novel end-to-end SfP method achieves robust single-shot surface normal estimation under diverse lighting, integrating PS priors and material segmentation.

HERT: a novel framework for semi-open 3D object retrieval using hierarchical hypergraph equilibrium, achieving state-of-the-art performance on four new benchmark datasets.

Self-Distilled Depth Refinement (SDDR) tackles noisy depth maps via a novel noisy Poisson fusion approach, achieving significant improvements in depth accuracy and edge quality.

SE(3)-bi-equivariant Transformers (BITR) revolutionizes point cloud assembly by guaranteeing robust alignment even with non-overlapping clouds, thanks to its unique equivariance properties.

SCube: Instant large-scale 3D scene reconstruction from sparse images using VoxSplats, a novel 3D Gaussian splat representation.