arXiv Computer Vision
MotionDLO: Hybrid Event- and Frame-Based Tracking of Deformable Linear Objects
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arXiv Computer Vision
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TrackDeform3D: Markerless and Autonomous 3D Keypoint Tracking and Dataset Collection for Deformable Objects
arXiv:2603.17068v3 Announce Type: replace Abstract: Structured 3D representations such as keypoints and meshes offer compact, expressive descriptions of deformable objects, jointly capturing geometri...
arXiv Computer Vision
Oct 22
RoPT: Robust 6DoF Object Pose Tracking in Video
arXiv:2610.28962v1 Announce Type: new Abstract: Accurate 6-DoF object pose tracking is essential for robotic interaction, yet conventional monocular pipelines frequently suffer from catastrophic dive...
arXiv Computer Vision
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DispFlow-GS: Displacement Flow Supervision with Motion Disentangling for Monocular Deformable 3D Gaussian Splatting
arXiv:2609.36940v1 Announce Type: new Abstract: Accurate dynamic scene reconstruction is important for robotic perception, where temporally consistent representations of dynamic environments are esse...
arXiv Machine Learning
Sep 16
TEMPO: Learning Temporal Context for Dynamic Robot Manipulation
arXiv:2609.16864v1 Announce Type: cross Abstract: Vision-language-action (VLA) models have achieved impressive performance in quasi-static manipulation, but struggle in dynamic manipulation tasks bec...
arXiv Computer Vision
Sep 3
TAPVid-MV: A Benchmark for Tracking Any Point in 3D Across Multiple Views
TAPVid-MV is a new benchmark for tracking any point in 3D across multiple synchronized camera views. It comprises 284 sequences, 1,142 calibrated camera streams, and 109,769 point tracks, covering indoor and outdoor domains and derived from various modalities such as depth, LiDAR, SLAM, and simulation. The dataset is visually verified, and evaluation shows that current multi‑view trackers do not consistently outperform monocular trackers, highlighting geometry recovery as a key bottleneck.
arXiv Computer Vision
Sep 23
Moving6DPoSe: A Multimodal Database for Monocular 6D Pose Estimation and Segmentation of Moving Objects
Moving6DPoSe is a multimodal database for monocular 6D pose estimation and segmentation of moving objects, comprising two subsets: real-world recordings (Moving6DPoSe‑R) and synthetic sequences (Moving6DPoSe‑S). It includes 16 scanned objects, 1,702 real and synthetic rosbags, and annotations for semantic segmentation, object detection, and monocular 6D pose estimation. Baseline results show that event-based representations outperform conventional RGB images for moving‑object segmentation, while monocular orientation estimation remains challenging.