NVAutoNet: Fast and Accurate 360$^{\circ}$ 3D Visual Perception For Self Driving

Robust, real-time perception of 3D world is essential to the autonomous vehicle. We introduce an end-to-end surround camera perception system, named NVAutoNet, for self-driving. NVAutoNet is a multi-task, multi-camera network which takes a variable set of time-synced camera images as input and produces a rich collection of 3D signals such as sizes, orientations, locations of obstacles, parking spaces and free-spaces, etc. NVAutoNet is modular and end-to-end: 1) the outputs can be consumed directly by downstream modules without any post-processing such as clustering and fusion -- improving speed of model deployment and in-car testing 2) the whole network training is done in one single stage -- improving speed of model improvement and iterations. The network is carefully designed to have high accuracy while running at 53 fps on NVIDIA Orin SoC (system-on-a-chip). The network is robust to sensor mounting variations (within some tolerances) and can be quickly customized for different vehicle types via efficient model fine-tuning.

Results in Papers With Code
(↓ scroll down to see all results)