  Although cameras are ubiquitous, robotic platforms typically rely on active
sensors like LiDAR for direct 3D perception. In this work, we propose a novel
self-supervised monocular depth estimation method combining geometry with a new
deep network, PackNet, learned only from unlabeled monocular videos. Our
architecture leverages novel symmetrical packing and unpacking blocks to
jointly learn to compress and decompress detail-preserving representations
using 3D convolutions. Although self-supervised, our method outperforms other
self, semi, and fully supervised methods on the KITTI benchmark. The 3D
inductive bias in PackNet enables it to scale with input resolution and number
of parameters without overfitting, generalizing better on out-of-domain data
such as the NuScenes dataset. Furthermore, it does not require large-scale
supervised pretraining on ImageNet and can run in real-time. Finally, we
release DDAD (Dense Depth for Automated Driving), a new urban driving dataset
with more challenging and accurate depth evaluation, thanks to longer-range and
denser ground-truth depth generated from high-density LiDARs mounted on a fleet
of self-driving cars operating world-wide.
