Tristan Rice

This is a follow up to 3D Semantic Segmentation and is part of a series where I try to train models to perform common self driving tasks from scratch. I decided to switch areas of focus for this new model. Previously I had been working entirely with dense models which output dense representations about the world such as the voxel occupancy grids and the BEV semantic maps for lane lines and drivable space.

This is a follow up to Voxel from Multicam and is part of a series where I try to train models to perform common self driving tasks from scratch. I’ve previously put together occupancy models for self driving but that’s only one specific perception task. Another common driving task is semantic segmentation. Semantic segmentation takes in the image and for every pixel predicts a specific class. This can be used to tell things like walls apart from cars or classify different types of lane lines and curbs on a road.

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.