Can I read Gated2Gated: Self-Supervised Depth Estimation from Gated Images on EtoBox?

Gated2Gated: Self-Supervised Depth Estimation from Gated Images by Walia, Amanpreet; Walz, Stefanie; Bijelic, Mario; Mannan, Fahim; Julca-Aguilar, Frank; Langer, Michael; Ritter, Werner; Heide, Felix is a scholarly article available to read on EtoBox.

What is Gated2Gated: Self-Supervised Depth Estimation from Gated Images about?

Gated cameras hold promise as an alternative to scanning LiDAR sensors with high-resolution 3D depth that is robust to back-scatter in fog, snow, and rain. Instead of sequentially scanning a scene and directly recording depth via the photon time-of-flight, as in pulsed LiDAR sensors, gated imagers encode depth in the relative intensity of a handful of gated slices, captured at megapixel resolution. Although existing methods have shown that it is possible to decode high-resolution depth from such measurements, these methods require synchronized and calibrated LiDAR to supervise the gated depth decoder -- prohibiting fast adoption across geographies, training on large unpaired datasets, and exploring alternative applications outside of automotive use cases. In this work, we fill this gap and propose an entirely self-supervised depth estimation method that uses gated intensity profiles and temporal consistency as a training signal. The proposed model is trained end-to-end from gated video sequences, does not require LiDAR or RGB data, and learns to estimate absolute depth values. We take gated slices as input and disentangle the estimation of the scene albedo, depth, and ambient light

Author

Walia, Amanpreet; Walz, Stefanie; Bijelic, Mario; Mannan, Fahim; Julca-Aguilar, Frank; Langer, Michael; Ritter, Werner; Heide, Felix

Published

2021

Language

EN