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F2G-Pose: Geometry-Aware Foundation Feature Lifting for Direct RGB-D Category-Level Object Pose Estimation

Paper recorded by Signals 4 on 2026-10-22 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-10-22 on arXiv · recorded by Signals 4 on 2026-10-23

Category: cs.CV · 计算机视觉 · first seen 2026-10-23

Abstract

This paper studies category-level RGB-D object pose estimation, which recovers an object's 3D rotation and translation without instance-specific CAD models or reference views. This task is highly ambiguous due to intra-class variations, symmetries, and partial observations. Current solutions struggle to balance reliability and efficiency: methods based on intermediate structures are prone to error

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#181 most recent of 756 cs.CV papers we have recorded · ↑ newer: HiP3R: Hierarchical Pose Graphs for Long-Horizon 3D Reconstruction · ↓ older: CellTrack-Bench: Microscopic Cell Tracking Benchmark for Vision-Langua
Cite this page: F2G-Pose: Geometry-Aware Foundation Feature Lifting for Direct RGB-D Category-Level Object Pose Estimation: the #181 most recent of 756 cs.CV papers we have recorded (as of 2026-10-22). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/f2g-pose-geometry-aware-foundation-feature-lifting-for-direct-rgb-d-category-lev.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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