# Depth Sensing Beyond LiDAR Range

arXiv: https://arxiv.org/abs/2004.03048 · HTML: https://mlsanity.com/paper/2004.03048/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 7 Apr 2020.

## Built on by (most cited first, 10 of 10)

- [Stereo Matching by Self-supervision of Multiscopic Vision](https://mlsanity.com/paper/2104.04170/) (2021)
- [Camera Calibration using a Collimator System](https://mlsanity.com/paper/2409.20034/) (2024)
- [Augmenting Depth Estimation with Geospatial Context](https://mlsanity.com/paper/2109.09879/) (2021)
- [3D Scene Inference from Transient Histograms](https://mlsanity.com/paper/2211.05094/) (2022)
- [Joint Prediction of Monocular Depth and Structure using Planar and Parallax Geometry](https://mlsanity.com/paper/2207.06351/) (2022)
- [Joint Depth and Normal Estimation from Real-world Time-of-flight Raw Data](https://mlsanity.com/paper/2108.03649/) (2021)
- [R4D: Utilizing Reference Objects for Long-Range Distance Estimation](https://mlsanity.com/paper/2206.04831/) (2022)
- [Towards Continual, Online, Self-Supervised Depth](https://mlsanity.com/paper/2103.00369/) (2021)
- [Revisiting Stereo Triangulation in UAV Distance Estimation](https://mlsanity.com/paper/2306.08939/) (2023)
- [Flexible Camera Calibration using a Collimator System](https://mlsanity.com/paper/2512.16113/) (2025)

## Abstract (arXiv)

Depth sensing is a critical component of autonomous driving technologies, but today's LiDAR- or stereo camera-based solutions have limited range. We seek to increase the maximum range of self-driving vehicles' depth perception modules for the sake of better safety. To that end, we propose a novel three-camera system that utilizes small field of view cameras. Our system, along with our novel algorithm for computing metric depth, does not require full pre-calibration and can output dense depth maps with practically acceptable accuracy for scenes and objects at long distances not well covered by most commercial LiDARs.