3D Human Pose and Shape Estimation from LiDAR Point Clouds: A Review
It addresses the problem of standardizing and advancing research in 3D human understanding from LiDAR data for researchers and practitioners, but it is incremental as it reviews and organizes existing work rather than introducing new methods.
This paper provides a comprehensive review of 3D human pose and shape estimation from LiDAR point clouds, comparing existing methods, proposing a taxonomy, and establishing benchmarks on key datasets to enable fair comparisons and advance the field.
In this paper, we present a comprehensive review of 3D human pose estimation and human mesh recovery from in-the-wild LiDAR point clouds. We compare existing approaches across several key dimensions, and propose a structured taxonomy to classify these methods. Following this taxonomy, we analyze each method's strengths, limitations, and design choices. In addition, (i) we perform a quantitative comparison of the three most widely used datasets, detailing their characteristics; (ii) we compile unified definitions of all evaluation metrics; and (iii) we establish benchmark tables for both tasks on these datasets to enable fair comparisons and promote progress in the field. We also outline open challenges and research directions critical for advancing LiDAR-based 3D human understanding. Moreover, we maintain an accompanying webpage that organizes papers according to our taxonomy and continuously update it with new studies: https://github.com/valeoai/3D-Human-Pose-Shape-Estimation-from-LiDAR