Person-MinkUNet: 3D Person Detection with LiDAR Point Cloud
arXiv:2107.06780v18 citations
Originality Synthesis-oriented
AI Analysis
This work addresses 3D person detection for robotics or autonomous systems, but it is incremental as it adapts existing methods to a specific task.
The paper tackled 3D person detection using LiDAR point clouds by applying submanifold sparse convolution, resulting in a network that achieved 76.4% average precision on the JRDB benchmark.
In this preliminary work we attempt to apply submanifold sparse convolution to the task of 3D person detection. In particular, we present Person-MinkUNet, a single-stage 3D person detection network based on Minkowski Engine with U-Net architecture. The network achieves a 76.4% average precision (AP) on the JRDB 3D detection benchmark.