JOINT OPTIMIZED POINT CLOUD COMPRESSION FOR 3D OBJECT DETECTION

As a large amount of point clouds are fed into machines for 3D object detection in autonomous driving, efficient point cloud compression methods are urgently needed. Existing point cloud compression methods are usually optimized for high signal fidelity rather than high detection accuracy. However, higher signal fidelity may be unnecessary for object detection. In this work, we propose a learning-based point cloud compression framework for 3D object detection by jointly optimizing the point cloud compression and 3D object detection network. Since point clouds commonly need to be pre-processed before performing detection, simply connecting the codec and detector will disable the gradient back-propagation due to some non-differentiable operations. Therefore, we design a gradient bridge function to enable the gradient back-propagation from the detector to the codec. In addition, joint optimization of two networks from scratch is easy to make the training process unstable. We propose a progressive training strategy to stabilize the training process. Experimental results demonstrate that our framework can achieve significant compression performance improvement under the same detection accuracy.