EgoRayFormer: 光線空間でのステレオ精緻化による一人称3D人体姿勢推定
EgoRayFormer: Ray-Space Stereo Refinement for Egocentric 3D Human Pose Estimation
頭部搭載ステレオ魚眼カメラの一人称視点で、各関節の3D仮説を光線空間上で両眼に投影・交差させて幾何学的に精緻化する姿勢推定手法を提案し、歪みや短基線の課題に対応した。
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著者: Haozhe Wang, Songyuan Wu, Jihai Xu, Jie Zhao
分類: cs.RO
原文アブストラクト
Head-mounted stereo fisheye cameras provide a compact view of the wearer's body, but strong distortion, partial visibility and a short baseline make 3D pose estimation difficult. Existing refinement decoders sample image features independently in each view, without requiring the samples to correspond to a common 3D hypothesis. We introduce EgoRayFormer, a causal pose estimator that couples stereo feature sampling and geometric refinement in ray space. For each joint, the decoder projects shared 3D hypotheses along and around its viewing ray into both images. A geometric branch then corrects the projected joint locations and intersects the resulting rays; a learned gate blends this geometric estimate with a learned pose update. This formulation introduces an explicit stereo constraint while retaining flexibility when the rays provide weak depth constraints. In local evaluations on the official device-relative test splits, EgoRayFormer achieves three-seed mean MPJPEs of 17.64 mm on UnrealEgo2 and 49.55 mm on UnrealEgo-RW, improving to 47.57 mm with UnrealEgo2 pre-training. In paired validation ablations, removing the geometric branch increases MPJPE in all three seeds at both input resolutions. With a common ResNet-18 encoder and input resolution, our system reduces RW test MPJPE by 9.09 mm relative to the device-relative EgoPoseFormer baseline. A separate protocol analysis quantifies the difference between device-relative and oracle-root evaluation.