HUM3DIL: Semi-supervised Multi-modal 3D Human Pose Estimation for Autonomous Driving
It addresses the problem of perceiving pedestrian behaviors for autonomous vehicles, representing an incremental advance by leveraging novel sensor data.
The paper tackles 3D human pose estimation for autonomous driving by proposing HUM3DIL, a semi-supervised multi-modal method that uses images and LiDAR, achieving state-of-the-art results on the Waymo Open Dataset with a large performance margin.
Autonomous driving is an exciting new industry, posing important research questions. Within the perception module, 3D human pose estimation is an emerging technology, which can enable the autonomous vehicle to perceive and understand the subtle and complex behaviors of pedestrians. While hardware systems and sensors have dramatically improved over the decades -- with cars potentially boasting complex LiDAR and vision systems and with a growing expansion of the available body of dedicated datasets for this newly available information -- not much work has been done to harness these novel signals for the core problem of 3D human pose estimation. Our method, which we coin HUM3DIL (HUMan 3D from Images and LiDAR), efficiently makes use of these complementary signals, in a semi-supervised fashion and outperforms existing methods with a large margin. It is a fast and compact model for onboard deployment. Specifically, we embed LiDAR points into pixel-aligned multi-modal features, which we pass through a sequence of Transformer refinement stages. Quantitative experiments on the Waymo Open Dataset support these claims, where we achieve state-of-the-art results on the task of 3D pose estimation.