By Prarthana Bhattacharyya, Chengjie Huang and Krzysztof Czarnecki.
We provide code support and configuration files to reproduce the results in the paper:
Self-Attention Based Context-Aware 3D Object Detection.
Our code is based on OpenPCDet, which is a clean open-sourced project for benchmarking 3D object detection methods.
Fig.1. Self-Attention augmented global-context aware backbone networks.
In this paper, we explore variations of self-attention for contextual modeling in 3D object detection by augmenting convolutional features with self-attention features. We first incorporate the pairwise self-attention mechanism into the current state-of-the-art BEV, voxel and point-based detectors and show consistent improvement over strong baseline models while simultaneously significantly reducing their parameter footprint and computational cost. We also propose a self-attention variant that samples a subset of the most representative features by learning deformations over randomly sampled locations. This not only allows us to scale explicit global contextual modeling to larger point-clouds, but also leads to more discriminative and informative feature descriptors.
Fig.2. Visualizing qualitative results between baseline and our proposed self-attention module.
Fig.3. 3D Car AP with respect to params and FLOPs of baseline and proposed self-attention variants.