DenseBEV: Transforming BEV Grid Cells into 3D Objects

TL;DR

DenseBEV introduces a novel end-to-end approach for 3D object detection using BEV feature cells as anchors, improving detection accuracy especially for small objects and achieving state-of-the-art results on major datasets.

Contribution

The paper proposes using BEV feature cells directly as anchors and incorporates a hybrid temporal modeling approach, enhancing efficiency and detection performance in multi-camera 3D object detection.

Findings

Significant improvements in NDS and mAP on nuScenes.

Enhanced pedestrian detection with 3.8% mAP increase.

State-of-the-art performance on Waymo dataset with 60.7% LET-mAP.

Abstract

In current research, Bird's-Eye-View (BEV)-based transformers are increasingly utilized for multi-camera 3D object detection. Traditional models often employ random queries as anchors, optimizing them successively. Recent advancements complement or replace these random queries with detections from auxiliary networks. We propose a more intuitive and efficient approach by using BEV feature cells directly as anchors. This end-to-end approach leverages the dense grid of BEV queries, considering each cell as a potential object for the final detection task. As a result, we introduce a novel two-stage anchor generation method specifically designed for multi-camera 3D object detection. To address the scaling issues of attention with a large number of queries, we apply BEV-based Non-Maximum Suppression, allowing gradients to flow only through non-suppressed objects. This ensures efficient training without the need for post-processing. By using BEV features from encoders such as BEVFormer directly as object queries, temporal BEV information is inherently embedded. Building on the temporal BEV information already embedded in our object queries, we introduce a hybrid temporal modeling approach by integrating prior detections to further enhance detection performance. Evaluating our method on the nuScenes dataset shows consistent and significant improvements in NDS and mAP over the baseline, even with sparser BEV grids and therefore fewer initial anchors. It is particularly effective for small objects, enhancing pedestrian detection with a 3.8% mAP increase on nuScenes and an 8% increase in LET-mAP on Waymo. Applying our method, named DenseBEV, to the challenging Waymo Open dataset yields state-of-the-art performance, achieving a LET-mAP of 60.7%, surpassing the previous best by 5.4%. Code is available at https://github.com/mdaehl/DenseBEV.