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PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Single Image

Paper recorded by Signals 4 on 2026-09-16 in cs.CV. Abstract reproduced from arXiv; link to the original below.

Published 2026-09-16 on arXiv · recorded by Signals 4 on 2026-09-17

Category: cs.CV · 计算机视觉 · first seen 2026-09-17

Abstract

Physical properties, such as friction, hardness, stiffness, and density, govern how robots should grasp, manipulate and interact with objects, yet estimating these properties from RGB images remains challenging. Existing methods typically employ per-object reconstruction augmented with physical properties or directly query vision-language models at test time, which results in substantial computati

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#20 most recent of 237 cs.CV papers we have recorded · ↑ newer: Track, Articulate, Act: Generating Articulation from Casual Human Vide · ↓ older: NormLift: From Lifted Features To Semantic Reliability In 3D Gaussian
Cite this page: PhysVGGT: Feed-Forward Dense Physical Property Estimation from A Single Image: the #20 most recent of 237 cs.CV papers we have recorded (as of 2026-09-16). Source: Signals 4 (Signals API) — https://data.jiangzhang.ca/signals4/t/papers/physvggt-feed-forward-dense-physical-property-estimation-from-a-single-image.html
Free to quote with attribution to “Signals 4 (Signals API)”. Machine-readable: papers.json
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