3 citations · 5 across the 7 of their papers we have counts for
8 papers
FAMOS: Feed-Forward 3D Articulation Modeling from Sparse Observations
Kevin Qu, Tao Sun, Massimiliano Viola +5
Modeling articulated objects from sparse monocular views is challenging because each observation reveals only partial geometry and motion evidence. Most feed-forward methods infer…
Unified Panoramic Geometry Estimation via Multi-View Foundation Models
Vukasin Bozic, Isidora Slavkovic, Dominik Narnhofer +4
Geometry estimation from perspective images has greatly advanced, maturing to the point where off-the-shelf foundation models are able to reconstruct 3D scene structure not only fr…
Need for Speed: Zero-Shot Depth Completion with Single-Step Diffusion
Jakub Gregorek, Paraskevas Pegios, Nando Metzger +3
We introduce Marigold-SSD, a single-step, late-fusion depth completion framework that leverages strong diffusion priors while eliminating the costly test-time optimization typicall…
Depth Completion as Parameter-Efficient Test-Time Adaptation
Bingxin Ke, Qunjie Zhou, Jiahui Huang +5
We introduce CAPA, a parameter-efficient test-time optimization framework that adapts pre-trained 3D foundation models (FMs) for depth completion, using sparse geometric cues. Unli…
LoopSplat: Loop Closure by Registering 3D Gaussian Splats
Liyuan Zhu, Yue Li, Erik Sandström +3
Simultaneous Localization and Mapping (SLAM) based on 3D Gaussian Splats (3DGS) has recently shown promise towards more accurate, dense 3D scene maps. However, existing 3DGS-based…
Living Scenes: Multi-object Relocalization and Reconstruction in Changing 3D Environments
Liyuan Zhu, Shengyu Huang, Konrad Schindler +1
Research into dynamic 3D scene understanding has primarily focused on short-term change tracking from dense observations, while little attention has been paid to long-term changes…
Dynamic LiDAR Re-simulation using Compositional Neural Fields
Hanfeng Wu, Xingxing Zuo, Stefan Leutenegger +3
We introduce DyNFL, a novel neural field-based approach for high-fidelity re-simulation of LiDAR scans in dynamic driving scenes. DyNFL processes LiDAR measurements from dynamic en…
Nothing Stands Still: A Spatiotemporal Benchmark on 3D Point Cloud Registration Under Large Geometric and Temporal Change
Tao Sun, Yan Hao, Shengyu Huang +4
Building 3D geometric maps of man-made spaces is a well-established and active field that is fundamental to computer vision and robotics. However, considering the evolving nature o…