PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest

TL;DR

PoIFusion introduces a multi-modal 3D object detection framework that fuses RGB and LiDAR data at points of interest, maintaining modality views and avoiding view transformation losses, achieving state-of-the-art results.

Contribution

The paper proposes PoIFusion, a novel fusion method at points of interest that preserves modality views and simplifies computation, outperforming existing methods on major datasets.

Findings

Achieves 74.9% NDS and 73.4% mAP on nuScenes

Achieves 31.6% CDS and 40.6% mAP on Argoverse2

Outperforms previous state-of-the-art methods

Abstract

In this work, we present PoIFusion, a conceptually simple yet effective multi-modal 3D object detection framework to fuse the information of RGB images and LiDAR point clouds at the points of interest (PoIs). Different from the most accurate methods to date that transform multi-sensor data into a unified view or leverage the global attention mechanism to facilitate fusion, our approach maintains the view of each modality and obtains multi-modal features by computation-friendly projection and interpolation. In particular, our PoIFusion follows the paradigm of query-based object detection, formulating object queries as dynamic 3D boxes and generating a set of PoIs based on each query box. The PoIs serve as the keypoints to represent a 3D object and play the role of the basic units in multi-modal fusion. Specifically, we project PoIs into the view of each modality to sample the corresponding feature and integrate the multi-modal features at each PoI through a dynamic fusion block. Furthermore, the features of PoIs derived from the same query box are aggregated together to update the query feature. Our approach prevents information loss caused by view transformation and eliminates the computation-intensive global attention, making the multi-modal 3D object detector more applicable. We conducted extensive experiments on nuScenes and Argoverse2 datasets to evaluate our approach. Remarkably, the proposed approach achieves state-of-the-art results on both datasets without any bells and whistles, \emph{i.e.}, 74.9\% NDS and 73.4\% mAP on nuScenes, and 31.6\% CDS and 40.6\% mAP on Argoverse2. The code will be made available at \url{https://djiajunustc.github.io/projects/poifusion}.