MoSoLoco: Monocular 3D Human Localization through Visual Latent Features and Social Context
Overview
This paper introduces MoSoLoco, a per-frame monocular 3D human localizer that combines a pose detector's internal visual features with detected 2D skeletons to estimate metric 3D positions without additional RGB or depth encoders. The method incorporates sparse same-frame multi-person context to refine individual position estimates. Experiments on JRDB and NuScenes show that reusing pose-detector representations reduces the best JRDB error by approximately 23 percent relative to the strongest evaluated keypoint-based baseline. A sparse social-interaction module incorporates nearby detections from the same image for further refinement. Controlled trajectory-forecasting experiments show downstream benefits, while the complete pipeline remains suitable for efficient Jetson AGX Xavier inference.
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- arXiv Robotics — research abstractsSignalMoSoLoco: Monocular 3D Human Localization through Visual Latent Features and Social Context
This paper introduces MoSoLoco, a per-frame monocular 3D human localizer that combines a pose detector's internal visual features with detected 2D skeletons to estimate metric 3D positions without additional RGB or depth encoders. The method incorporates sparse same-frame multi-person context to refine individual position estimates. Experiments on JRDB and NuScenes show that reusing pose-detector representations reduces the best JRDB error by approximately 23 percent relative to the strongest evaluated keypoint-based baseline. A sparse social-interaction module incorporates nearby detections from the same image for further refinement. Controlled trajectory-forecasting experiments show downstream benefits, while the complete pipeline remains suitable for efficient Jetson AGX Xavier inference.
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