arXiv Computer Vision
RoPT: Robust 6DoF Object Pose Tracking in Video
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arXiv Computer Vision
Oct 9
Fast Pose Tracking of Rigid Objects with Compact Pose Graph Optimization
The paper presents a lightweight 6D pose tracking method that operates on relative pose constraints instead of point-based optimization, using uncertainty derived from geometric alignment to weight these constraints. By avoiding direct inclusion of noisy point measurements, the approach achieves fast and robust long-term tracking with computational costs far lower than reconstruction-based trackers. Experiments on four real-world benchmarks show comparable accuracy to state-of-the-art methods while maintaining real-time performance.
arXiv Computer Vision
Sep 22
AgentSTAR: Agentic Shape Tracking and Reconstruction from Monocular Videos
arXiv:2609.24487v1 Announce Type: new Abstract: In this work, we present a method for shape reconstruction and tracking from video via agentic analysis-by-synthesis. Unlike prior methods which first...
arXiv Computer Vision
Sep 14
Dense Dynamic Scene Reconstruction and Camera Pose Estimation from Multi-View Videos
arXiv:2603.12064v3 Announce Type: replace Abstract: We address the challenging problem of dense dynamic scene reconstruction and camera pose estimation from multiple freely moving cameras -- a settin...
arXiv Computer Vision
Sep 24
Track2Art: Motion-Centric Articulated Object Model Recovery from 2D Point Trackers
Track2Art is a motion‑centric framework that recovers articulated object models from RGB‑D interaction videos by lifting 2D point tracks into 3D trajectories. It groups these trajectories into rigid‑part hypotheses and uses learned‑analytic reasoning to infer directed kinematic relations, joint types, and joint geometry. On the PartNet‑Mobility benchmark, it achieves 0.695 Point IoU and 0.410 end‑to‑end J@20 without requiring ground‑truth part counts or test‑time optimization.
arXiv Computer Vision
1d ago
ReLift: Dense 3D Tracking as Lifted Trajectory Repair
ReLift presents a feed‑forward framework that treats dense 3D tracking as a problem of repairing and completing lifted trajectories. By alternating attention along and across trajectories, it corrects depth and correspondence errors, and fills in missing motion, all while training solely on simulated 3D trajectory data. The method improves lifted trajectories from any 2D tracker or geometry model, achieving state‑of‑the‑art accuracy on WorldTrack and running faster than leading 4D trackers.
arXiv Computer Vision
Sep 28
Track2Art: Articulated Object Model Recovery with Visual-Geometric Track Representations
arXiv:2609.27675v2 Announce Type: replace Abstract: Understanding articulated objects is fundamental for robotic interaction, requiring accurate rigid-part discovery and the recovery of their kinemat...