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This research aims to develop an occlusion-aware framework for improving human pose estimation in the presence of occlusions using point clouds.

Occlusion-robust human pose estimation with synthetic occlusion in point clouds

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Randomized trial reports enhanced pose estimation performance amid occlusions in robots, suggesting simulation benefits.

This research aims to develop an occlusion-aware framework for improving human pose estimation in the presence of occlusions using point clouds.
Developed a framework combining PointNet++ for feature extraction and a Transformer for temporal encoding.
Used a graph convolutional network with inverse DCT for skeletal reconstruction from occluded point clouds.
Evaluated the method against an RGB-based baseline under robot-induced occlusions using 128 annotated frames.
Proposed method achieved lower estimation errors at the shoulder and elbow compared to the baseline.
Occlusion augmentation significantly improved performance, reducing sensitivity to occlusions.
Error-visibility correlations remained for the baseline but not for the new method, indicating better robustness.

TAKASE et al. (2026) studied this question.

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View Full Paper

Ask AI

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Authors

Randomized trial reports enhanced pose estimation performance amid occlusions in robots, suggesting simulation benefits.

This research aims to develop an occlusion-aware framework for improving human pose estimation in the presence of occlusions using point clouds.
Developed a framework combining PointNet++ for feature extraction and a Transformer for temporal encoding.
Used a graph convolutional network with inverse DCT for skeletal reconstruction from occluded point clouds.
Evaluated the method against an RGB-based baseline under robot-induced occlusions using 128 annotated frames.
Proposed method achieved lower estimation errors at the shoulder and elbow compared to the baseline.
Occlusion augmentation significantly improved performance, reducing sensitivity to occlusions.
Error-visibility correlations remained for the baseline but not for the new method, indicating better robustness.

TAKASE et al. (2026) studied this question.

View Full Paper

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