3D object detection and dense depth estimation are one of the most vital
tasks in autonomous driving. Multiple sensor modalities can jointly attribute
towards better robot perception, and to that end, we introduce a method for
jointly training 3D object detection and monocular dense depth reconstruction
neural networks. It takes as inputs, a LiDAR point-cloud, and a single RGB
image during inference and produces object pose predictions as well as a
densely reconstructed depth map. LiDAR point-cloud is converted into a set of
voxels, and its features are extracted using 3D convolution layers, from which
we regress object pose parameters. Corresponding RGB image features are
extracted using another 2D convolutional neural network. We further use these
combined features to predict a dense depth map. While our object detection is
trained in a supervised manner, the depth prediction network is trained with
both self-supervised and supervised loss functions. We also introduce a loss
function, edge-preserving smooth loss, and show that this results in better
depth estimation compared to the edge-aware smooth loss function, frequently
used in depth prediction works.

介绍了一种联合训练 3D 目标检测和单目稠密深度重建神经网络的方法，通过 LiDAR 点云和单个 RGB 图像生成物体姿态预测和密集重建深度，其中 LiDAR 点云被转换为一组体素并使用 3D 卷积层提取特征，通过另一个 2D 卷积神经网络提取相应的 RGB 图像特征，并使用这些组合特征来预测密集深度图。