# Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios

arXiv: https://arxiv.org/abs/1906.08953 · HTML: https://mlsanity.com/paper/1906.08953/

## Key facts

- Cited by 12 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 21 Jun 2019.

## Built on by (most cited first, 12 of 12)

- [ScatterNeRF: Seeing Through Fog with Physically-Based Inverse Neural Rendering](https://mlsanity.com/paper/2305.02103/) (2023)
- [DurLAR: A High-fidelity 128-channel LiDAR Dataset with Panoramic Ambient and Reflectivity Imagery for Multi-modal Autonomous Driving Applications](https://mlsanity.com/paper/2406.10068/) (2024)
- [LiDARTouch: Monocular metric depth estimation with a few-beam LiDAR](https://mlsanity.com/paper/2109.03569/) (2021)
- [Gated Stereo: Joint Depth Estimation from Gated and Wide-Baseline Active Stereo Cues](https://mlsanity.com/paper/2305.12955/) (2023)
- [Gated2Gated: Self-Supervised Depth Estimation from Gated Images](https://mlsanity.com/paper/2112.02416/) (2021)
- [An Unsupervised Domain Adaptive Approach for Multimodal 2D Object Detection in Adverse Weather Conditions](https://mlsanity.com/paper/2203.03568/) (2022)
- [SLiDE: Self-supervised LiDAR De-snowing through Reconstruction Difficulty](https://mlsanity.com/paper/2208.04043/) (2022)
- [ZeroScatter: Domain Transfer for Long Distance Imaging and Vision through Scattering Media](https://mlsanity.com/paper/2102.05847/) (2021)
- [RiWNet: A moving object instance segmentation Network being Robust in adverse Weather conditions](https://mlsanity.com/paper/2109.01820/) (2021)
- [Structure-Centric Robust Monocular Depth Estimation via Knowledge Distillation](https://mlsanity.com/paper/2410.06982/) (2024)
- [AURORA-KITTI: Any-Weather Depth Completion and Denoising](https://mlsanity.com/paper/2603.14701/) (2026)
- [Estimating Fog Parameters from a Sequence of Stereo Images](https://mlsanity.com/paper/2511.20865/) (2025)

## Abstract (arXiv)

This work introduces an evaluation benchmark for depth estimation and completion using high-resolution depth measurements with angular resolution of up to 25" (arcsecond), akin to a 50 megapixel camera with per-pixel depth available. Existing datasets, such as the KITTI benchmark, provide only sparse reference measurements with an order of magnitude lower angular resolution - these sparse measurements are treated as ground truth by existing depth estimation methods. We propose an evaluation methodology in four characteristic automotive scenarios recorded in varying weather conditions (day, night, fog, rain). As a result, our benchmark allows us to evaluate the robustness of depth sensing methods in adverse weather and different driving conditions. Using the proposed evaluation data, we demonstrate that current stereo approaches provide significantly more stable depth estimates than monocular methods and lidar completion in adverse weather. Data and code are available at https://github.com/gruberto/PixelAccurateDepthBenchmark.git.