6 citations · 6 across the 3 of their papers we have counts for
9 papers · 1 filter
ConeGS: Error-Guided Densification Using Pixel Cones for Improved Reconstruction With Fewer Primitives
Bartłomiej Baranowski, Stefano Esposito, Patricia Gschoßmann +2
3D Gaussian Splatting (3DGS) achieves state-of-the-art image quality and real-time performance in novel view synthesis but often suffers from a suboptimal spatial distribution of p…
Human3R: Everyone Everywhere All at Once
Yue Chen, Xingyu Chen, Yuxuan Xue +3
We present Human3R, a unified, feed-forward framework for online 4D human-scene reconstruction, in the world frame, from casually captured monocular videos. Unlike previous approac…
TTT3R: 3D Reconstruction as Test-Time Training
Xingyu Chen, Yue Chen, Yuliang Xiu +2
Modern Recurrent Neural Networks have become a competitive architecture for 3D reconstruction due to their linear-time complexity. However, their performance degrades significantly…
LoftUp: Learning a Coordinate-Based Feature Upsampler for Vision Foundation Models
Haiwen Huang, Anpei Chen, Volodymyr Havrylov +2
Vision foundation models (VFMs) such as DINOv2 and CLIP have achieved impressive results on various downstream tasks, but their limited feature resolution hampers performance in ap…
GenFusion: Closing the Loop between Reconstruction and Generation via Videos
Sibo Wu, Congrong Xu, Binbin Huang +2
Recently, 3D reconstruction and generation have demonstrated impressive novel view synthesis results, achieving high fidelity and efficiency. However, a notable conditioning gap ca…
Easi3R: Estimating Disentangled Motion from DUSt3R Without Training
Xingyu Chen, Yue Chen, Yuliang Xiu +2
Recent advances in DUSt3R have enabled robust estimation of dense point clouds and camera parameters of static scenes, leveraging Transformer network architectures and direct super…