RESOLVE: A Multi-Resolution and Multi-Modal Dataset for Roadside Cooperative Perception

2026-06-30

AI Summary

This study addresses the performance degradation caused by LiDAR point cloud sparsity in roadside cooperative perception. To this end, the authors construct a large-scale, real-world, multi-resolution, and multimodal dataset comprising synchronized camera images and LiDAR data captured at three distinct resolutions, under diverse lighting and weather conditions, along with 220,000 meticulously annotated 3D bounding boxes. For the first time, they systematically evaluate both single-modality and camera–LiDAR fusion approaches for 3D detection and tracking under controlled environmental variables across varying point cloud densities. Their analysis reveals the impact mechanisms of resolution variation, sensing range, and train–test mismatch. Experimental results demonstrate that multimodal fusion significantly mitigates the adverse effects of point cloud sparsity, offering critical insights for designing cost-effective and high-performance roadside perception systems.

Abstract

LiDAR has increasingly been integrated into traffic cameras to expand coverage and mitigate occlusion in roadside cooperative perception. However, how unimodal and camera-LiDAR fusion architectures behave under variations in LiDAR point sparsity induced by sensor configurations and scene-dependent sensing conditions remains underexplored. We introduce RESOLVE, a large-scale real-world benchmark dataset featuring multi-resolution roadside LiDAR and synchronized camera-LiDAR sensing for systematic evaluation of unimodal and fusion-based architectures in roadside 3D detection and tracking. RESOLVE contains over 100k images and 26k point cloud frames with 220k manually annotated bounding boxes, captured at a real-world urban intersection across diverse lighting and weather conditions and spanning 10 classes of traffic participants. In particular, RESOLVE enables controlled evaluation across three LiDAR resolution levels while keeping all other sensing and environmental factors fixed. This allows fair cross-architecture comparisons under point cloud distribution shifts resulting from resolution variations, sensing distance, and training-inference resolution mismatches. Results from extensive benchmark experiments reveal insights into how multimodal fusion can compensate for LiDAR point sparsity, offering clues for designing cost-efficient roadside multimodal perception. The dataset and benchmark codes are available at https://github.com/ASU-Suo-Lab/RESOLVE.

Problem

• roadside cooperative perception
• LiDAR point sparsity
• camera-LiDAR fusion
• multi-resolution sensing
• 3D detection

Innovation

• multi-resolution LiDAR
• camera-LiDAR fusion
• roadside cooperative perception
• point cloud sparsity
• controlled benchmarking