Reliable and accurate 3D object detection is a necessity for safe autonomous
driving. Although LiDAR sensors can provide accurate 3D point cloud estimates
of the environment, they are also prohibitively expensive for many settings.
Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction
in the accuracy gap between methods based on LiDAR sensors and those based on
cheap stereo cameras. PL combines state-of-the-art deep neural networks for 3D
depth estimation with those for 3D object detection by converting 2D depth map
outputs to 3D point cloud inputs. However, so far these two networks have to be
trained separately. In this paper, we introduce a new framework based on
differentiable Change of Representation (CoR) modules that allow the entire PL
pipeline to be trained end-to-end. The resulting framework is compatible with
most state-of-the-art networks for both tasks and in combination with PointRCNN
improves over PL consistently across all benchmarks -- yielding the highest
entry on the KITTI image-based 3D object detection leaderboard at the time of
submission. Our code will be made available at
this https URL

本文提出了一种基于可微分的表示变换模块的框架，使得 3D 物体检测与深度估计的深度神经网络能够在此统一的框架中进行端到端的训练，该框架与大多数目前最先进的神经网络兼容，并在 KITTI 基于图像的 3D 物体检测排行榜上获得最高名次。