  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.
