Pyramid R-CNN: Towards Better Performance and Adaptability for 3D Object Detection

Understand

We present a flexible and high-performance framework, named Pyramid R-CNN, for two-stage 3D object detection from point clouds.

• Current approaches generally rely on the points or voxels of interest for RoI feature extraction on the second stage, but cannot effectively handle the sparsity and non-uniform distribution of those points, and this may result in failures in detecting objects that are far away.
• To resolve the problems, we propose a novel second-stage module, named pyramid RoI head, to adaptively learn the features from the sparse points of interest.
• The pyramid RoI head consists of three key components.