This is a follow up to Monocular Depth Improvements and is part of a series where I try to train models to perform common self driving tasks from scratch.
Background I spent a couple of months optimizing single camera (monocular) depth models before realizing that maybe there’s a better way. One of the biggest improvements I made to the monocular models was adding a 3D geometric constraint to enforce that the model didn’t predict depths below the ground.
This is a follow up to DIY Self Driving.
In the past few months I’ve been iterating on my previous work on creating self driving models. The main goals were initially:
train depth models for each camera generate joint point clouds from the multiple cameras use the fused outputs to create a high quality reconstruction that I can use to label things like lane lines This post lists all the various problems I ran into and some of the mitigations I applied for those issues.
This work was done in collaboration with green, Sherman and Sid.
During the holidays I decided to take some time and try to make my own self driving machine learning models as part of a pipe dream to have an open source self driving car. I hacked this together over the course of about 2 weeks in between holiday activities.
Disclaimer 1: I’m a software engineer on PyTorch but this work was done on my own time and not part of my normal duties.