Scene Reconstruction as Mapping Priors for 3D Detection

2026-05-21

AI Summary

High-definition maps are costly to produce, and existing 3D detection methods struggle to effectively leverage environmental structural priors to address challenges such as sparse and noisy sensor data or adverse weather conditions. This work proposes the MPA3D framework, which automatically constructs dense, annotation-free scene reconstructions from multi-frame LiDAR and image data to serve as mapping priors. These priors are systematically integrated into an end-to-end 3D object detection pipeline, enabling deep fusion of multimodal features and structural context. Evaluated on the Waymo Open Dataset, the approach achieves state-of-the-art performance, demonstrating that scalable reconstruction priors provide significant benefits for 3D detection accuracy and robustness.

Abstract

In autonomous driving, mapping is critical for motion planning but remains an under-utilized resource for perception tasks such as 3D object detection. Maps can provide robust structural priors of the static environment, helping resolve ambiguities and correct for sensor data sparsity or noise, especially for distant objects or under adverse weather conditions. However, conventional High-Definition (HD) maps are resource-intensive to obtain and maintain, which presents a challenge for efficient, large-scale deployment. In this paper, we propose a scalable solution to systematically leverage mapping to improve 3D detection by overcoming two primary challenges. First, we introduce a pipeline to automatically build dense mapping priors from aggregated sensor data, eliminating the need for human labeling. Second, we design a novel Mapping Priors Augmented 3D Detection (MPA3D) framework to effectively integrate mapping priors with different sensor modalities. Extensive experiments on the Waymo Open Dataset demonstrate that our approach achieves new state-of-the-art results, proving the effectiveness of scalable reconstructed scene priors for enhancing 3D detection.

Problem

• 3D object detection
• HD maps
• mapping priors
• autonomous driving
• scene reconstruction

Innovation

• scene reconstruction
• mapping priors
• 3D object detection
• scalable perception
• MPA3D