# Gated2Depth: Real-time Dense Lidar from Gated Images

## TL;DR

This paper introduces Gated2Depth, a real-time, low-cost dense depth estimation method converting three gated images into high-resolution depth maps, rivaling pulsed lidar in accuracy and range, while overcoming limitations of traditional scanning lidar systems.

## Contribution

It presents a novel deep learning framework that transforms low-cost gated camera images into dense depth maps without dense labels, enabling long-range, real-time depth sensing.

## Key findings

- Achieves at least 80m range with high accuracy
- Operates in real-time on standard hardware
- Validated with over 4,000km of real-world driving data

## Abstract

We present an imaging framework which converts three images from a gated camera into high-resolution depth maps with depth accuracy comparable to pulsed lidar measurements. Existing scanning lidar systems achieve low spatial resolution at large ranges due to mechanically-limited angular sampling rates, restricting scene understanding tasks to close-range clusters with dense sampling. Moreover, today's pulsed lidar scanners suffer from high cost, power consumption, large form-factors, and they fail in the presence of strong backscatter. We depart from point scanning and demonstrate that it is possible to turn a low-cost CMOS gated imager into a dense depth camera with at least 80m range - by learning depth from three gated images. The proposed architecture exploits semantic context across gated slices, and is trained on a synthetic discriminator loss without the need of dense depth labels. The proposed replacement for scanning lidar systems is real-time, handles back-scatter and provides dense depth at long ranges. We validate our approach in simulation and on real-world data acquired over 4,000km driving in northern Europe. Data and code are available at https://github.com/gruberto/Gated2Depth.