mlsanityData to 14 Oct 2026

HOISDF: Constraining 3D Hand-Object Pose Estimation with Global Signed Distance Fields

arXiv 2402.17062 ↗First posted 26 Feb 2024cs.CVcited by 14

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Abstract (arXiv)

Human hands are highly articulated and versatile at handling objects. Jointly estimating the 3D poses of a hand and the object it manipulates from a monocular camera is challenging due to frequent occlusions. Thus, existing methods often rely on intermediate 3D shape representations to increase performance. These representations are typically explicit, such as 3D point clouds or meshes, and thus provide information in the direct surroundings of the intermediate hand pose estimate. To address this, we introduce HOISDF, a Signed Distance Field (SDF) guided hand-object pose estimation network, which jointly exploits hand and object SDFs to provide a global, implicit representation over the complete reconstruction volume. Specifically, the role of the SDFs is threefold: equip the visual encoder with implicit shape information, help to encode hand-object interactions, and guide the hand and object pose regression via SDF-based sampling and by augmenting the feature representations. We show that HOISDF achieves state-of-the-art results on hand-object pose estimation benchmarks (DexYCB and HO3Dv2). Code is available at https://github.com/amathislab/HOISDF

Key facts. Cited by 14 papers in the map (data through 14 Oct 2026). Builds on 45 of its 59 references that are in the map. First posted on arXiv 26 Feb 2024.

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45 of 59 references.

References · 45

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33 more
  1. 2020INeRF: Inverting Neural Radiance Fields for Pose Estimationcited by 382
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  3. 2022HOI4D: A 4D Egocentric Dataset for Category-Level Human-Object Interactioncited by 327
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Citing papers per year: 2024: 4; 2025: 7; 2026: 3.

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