LXL: LiDAR Excluded Lean 3D Object Detection with 4D Imaging Radar and Camera Fusion

TL;DR

This paper introduces LXL, a novel 3D object detection method that fuses 4D radar, camera, and image depth data using a sampling view transformation strategy, achieving superior results without LiDAR.

Contribution

The paper proposes a new fusion approach that leverages image depth and radar occupancy to enhance sampling-based view transformation in 3D detection.

Findings

Outperforms state-of-the-art methods on VoD and TJ4DRadSet datasets.

Effective use of radar occupancy and image depth improves detection accuracy.

Sampling strategy benefits from integrating multiple sensor modalities.

Abstract

As an emerging technology and a relatively affordable device, the 4D imaging radar has already been confirmed effective in performing 3D object detection in autonomous driving. Nevertheless, the sparsity and noisiness of 4D radar point clouds hinder further performance improvement, and in-depth studies about its fusion with other modalities are lacking. On the other hand, as a new image view transformation strategy, "sampling" has been applied in a few image-based detectors and shown to outperform the widely applied "depth-based splatting" proposed in Lift-Splat-Shoot (LSS), even without image depth prediction. However, the potential of "sampling" is not fully unleashed. This paper investigates the "sampling" view transformation strategy on the camera and 4D imaging radar fusion-based 3D object detection. LiDAR Excluded Lean (LXL) model, predicted image depth distribution maps and radar 3D occupancy grids are generated from image perspective view (PV) features and radar bird's eye view (BEV) features, respectively. They are sent to the core of LXL, called "radar occupancy-assisted depth-based sampling", to aid image view transformation. We demonstrated that more accurate view transformation can be performed by introducing image depths and radar information to enhance the "sampling" strategy. Experiments on VoD and TJ4DRadSet datasets show that the proposed method outperforms the state-of-the-art 3D object detection methods by a significant margin without bells and whistles. Ablation studies demonstrate that our method performs the best among different enhancement settings.