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Reconstructing Hand-Held Objects from Monocular Video

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Di Huang, Xiaopeng Ji, Xingyi He, Jiaming Sun, Tong He, Qing Shuai, Wanli Ouyang, Xiaowei Zhou

Computer Vision and Pattern Recognition · Submitted 30 Nov 2022

abstract compiled · 2026-09-23 · no language-model review

The authors' main claim In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry.

how it is stated · asserted 1.00 read the record →

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arXiv ID2211.16835 (version 1)
Pith ID64FWYSWR, Pith's permanent record of this paper and its review
CitationDi Huang, Jiaming Sun, Qing Shuai, Tong He, Wanli Ouyang, Xiaopeng Ji, Xiaowei Zhou, Xingyi He. "Reconstructing Hand-Held Objects from Monocular Video." arXiv:2211.16835 (2022). https://pith.science/paper/64FWYSWR
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The paper's own sentences, arranged. Each label is drawn from a closed vocabulary; the number beside it is the probability the classifier gave that label. Numbers in the tables are the authors', checked by code. No sentence in this record was written by a model.

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Compiled by classifier-1.13.0 via TypeSafe API, question bank abstract-p1-20260923, 2026-09-23: 1 request, 4,862 input tokens.

What the abstract itself states, as probabilities: claims first 0.04 · states limitation 0.32 · human participants 0.35 · controlled comparison 0.27 · randomized assignment 0.01 · states quantitative result 0.05 · compares with prior results 0.78 · mentions code or data availability 0.14.

The abstract, sentence by sentence

arXiv listing abstract v1. Results and the main claim are underlined; .

This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. Then, the object geometry can be recovered by solving a multi-view reconstruction problem. We devise an implicit neural representation-based method to solve the reconstruction problem and address the issues of imprecise hand pose estimation, relative hand-object motion, and insufficient geometry optimization for small objects. We also provide a newly collected dataset with 3D ground truth to validate the proposed approach.

background contribution method result interpretation main claim

What the classifier reports for each sentence. The number is the probability it gave the label.
#sentencerolethe authors' own workstates the main conclusionstrength
s0 This paper presents an approach that reconstructs a hand-held object from a monocular video. contribution 1.00 0.91 0.22 asserted 1.00
s1 In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. contribution 0.90 0.61 0.38 asserted 1.00
s2 The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. method 0.97 0.34 0.07 asserted 0.99
s3 Then, the object geometry can be recovered by solving a multi-view reconstruction problem. method 0.99 0.43 0.08 asserted 0.95
s4 We devise an implicit neural representation-based method to solve the reconstruction problem and address the issues of imprecise hand pose estimation, relative hand-object motion, and insufficient geometry optimization for small objects. method 0.83 0.96 0.11 asserted 1.00
s5 We also provide a newly collected dataset with 3D ground truth to validate the proposed approach. contribution 0.97 0.97 0.06 asserted 1.00

What kind of paper the classifier reports this is: method or tool 1.00 · evidence from observation or experiment 0.96.

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Every quoted sentence on this record is the authors'. Every label is from Pith's closed vocabulary, with the probability shown beside it. Every number in the measurements table is the authors', checked by code. Record as JSON

The paper
Original abstract

This paper presents an approach that reconstructs a hand-held object from a monocular video. In contrast to many recent methods that directly predict object geometry by a trained network, the proposed approach does not require any learned prior about the object and is able to recover more accurate and detailed object geometry. The key idea is that the hand motion naturally provides multiple views of the object and the motion can be reliably estimated by a hand pose tracker. Then, the object geometry can be recovered by solving a multi-view reconstruction problem. We devise an implicit neural representation-based method to solve the reconstruction problem and address the issues of imprecise hand pose estimation, relative hand-object motion, and insufficient geometry optimization for small objects. We also provide a newly collected dataset with 3D ground truth to validate the proposed approach.

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