# Pixel-Accurate Depth Evaluation in Realistic Driving Scenarios

## TL;DR

This paper presents a high-resolution depth evaluation benchmark for realistic driving scenarios, enabling assessment of depth estimation robustness under various weather conditions and comparing stereo, monocular, and lidar methods.

## Contribution

It introduces a novel evaluation benchmark with high-resolution depth data for automotive scenarios, addressing limitations of existing sparse datasets.

## Key findings

- Stereo methods outperform monocular in adverse weather.
- The benchmark reveals robustness differences among depth sensing techniques.
- Current methods vary significantly in stability across conditions.

## Abstract

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.