Scene Reconstruction as Mapping Priors for 3D Detection
Quick Take
This paper presents a novel Mapping Priors Augmented 3D Detection (MPA3D) framework that utilizes dense mapping priors from aggregated sensor data to enhance 3D object detection in autonomous driving. By eliminating the need for human labeling and integrating various sensor modalities, the approach achieves state-of-the-art results on the Waymo Open Dataset, addressing challenges of sensor data sparsity and environmental ambiguities.
Key Points
- Introduces a scalable method for dense mapping priors without human labeling.
- MPA3D framework integrates mapping priors with multiple sensor modalities.
- Achieves state-of-the-art performance on the Waymo Open Dataset.
- Addresses challenges of sensor data sparsity and adverse weather conditions.
- Enhances 3D object detection in autonomous driving applications.
Article Content
From source RSS / original summaryarXiv:2605. 22997v1 Announce Type: new 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.
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