ARXIV:2603.16343 · 3D HUMAN POSE ESTIMATION · SUBMITTED 02 APR · 02:30 UTC · FRESHNESS STALE

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Learning Human-Object Interaction for 3D Human Pose Estimation from LiDAR Point Clouds

arXiv

A framework for robust 3D human pose estimation from LiDAR point clouds leveraging human-object interactions.

Blocked on Code›Score7.0Evidence unverified

Opportunity summary

Pain A framework for robust 3D human pose estimation from LiDAR point clouds leveraging human-object interactions.

Evidence 0 refs | 0 sources | 17% coverage

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PROBLEM

A framework for robust 3D human pose estimation from LiDAR point clouds leveraging human-object interactions. Nevertheless, existing methods largely overlook the potential of leveraging human-object interactions to build robust 3D human pose estimation frameworks.

METHOD

Full abstract

Understanding humans from LiDAR point clouds is one of the most critical tasks in autonomous driving due to its close relationships with pedestrian safety, yet it remains challenging in the presence of diverse human-object interactions and cluttered backgrounds. Nevertheless, existing methods largely overlook the potential of leveraging human-object interactions to build robust 3D human pose estimation frameworks. There are two major challenges that motivate the incorporation of human-object interaction. First, human-object interactions introduce spatial ambiguity between human and object points, which often leads to erroneous 3D human keypoint predictions in interaction regions. Second, there exists severe class imbalance in the number of points between interacting and non-interacting body parts, with the interaction-frequent regions such as hand and foot being sparsely observed in LiDAR data. To address these challenges, we propose a Human-Object Interaction Learning (HOIL) framework for robust 3D human pose estimation from LiDAR point clouds. To mitigate the spatial ambiguity issue, we present human-object interaction-aware contrastive learning (HOICL) that effectively enhances feature discrimination between human and object points, particularly in interaction regions. To alleviate the class imbalance issue, we introduce contact-aware part-guided pooling (CPPool) that adaptively reallocates representational capacity by compressing overrepresented points while preserving informative points from interacting body parts. In addition, we present an optional contact-based temporal refinement that refines erroneous per-frame keypoint estimates using contact cues over time. As a result, our HOIL effectively leverages human-object interaction to resolve spatial ambiguity and class imbalance in interaction regions. Codes will be released.

RESULT

ScienceToStartup currently rates this 7.0/10 on the public viability pass. As a result, our HOIL effectively leverages human-object interaction to resolve spatial ambiguity and class imbalance in interaction regions.

WHY NOW

3D Human Pose Estimation moved forward this cycle; last verified April 2026. Public score 7.0/10.

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Opportunity summary

Score7.0

PainA framework for robust 3D human pose estimation from LiDAR point clouds leveraging human-object interactions.

Evidence0 refs | 0 sources | 17% coverage

Blockermissing authors

Analysis summary

A framework for robust 3D human pose estimation from LiDAR point clouds leveraging human-object interactions.

VerifiedSource: PDF linkedPartialPaperPack: 3 of 4 citation fields filledMissingMissing fields: authorsPartialProof: unverified proof status

Competitive landscape

A framework for robust 3D human pose estimation from LiDAR point clouds leveraging human-object interactions.

Segment

3D Human Pose Estimation

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

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Competitive landscape

A framework for robust 3D human pose estimation from LiDAR point clouds leveraging human-object interactions.

Segment

3D Human Pose Estimation

Adoption evidence

No public code link in the paper record yet

Commercial read

7.0/10 public viability

Direct

not classified

Adjacent

not classified

Substitute

not classified

Unknown

not classified

Learning Human-Object Interaction for 3D Human Pose Estimation from LiDAR Point Clouds

Learning Human-Object Interaction for 3D Human Pose Estimation from LiDAR Point Clouds

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