# SimpleBEV: Improved LiDAR-Camera Fusion Architecture for 3D Object Detection Source: [SimpleBEV: Improved LiDAR-Camera Fusion Architecture for 3D Object Detection]() Collection index: [llms.txt]() Data updated: 2025-10-26T03:38:44.922Z Authors: Yun Zhao, Zhan Gong, Peiru Zheng, Hong Zhu, Shaohua Wu Published: 2024-11-08T02:51:39.000Z ## Abstract More and more research works fuse the LiDAR and camera information to improve the 3D object detection of the autonomous driving system. Recently, a simple yet effective fusion framework has achieved an excellent detection performance, fusing the LiDAR and camera features in a unified bird's-eye-view (BEV) space. In this paper, we propose a LiDAR-camera fusion framework, named SimpleBEV, for accurate 3D object detection, which follows the BEV-based fusion framework and improves the camera and LiDAR encoders, respectively. Specifically, we perform the camera-based depth estimation using a cascade network and rectify the depth results with the depth information derived from the LiDAR points. Meanwhile, an auxiliary branch that implements the 3D object detection using only the camera-BEV features is introduced to exploit the camera information during the training phase. Besides, we improve the LiDAR feature extractor by fusing the multi-scaled sparse convolutional features. Experimental results demonstrate the effectiveness of our proposed method. Our method achieves 77.6\\% NDS accuracy on the nuScenes dataset, showcasing superior performance in the 3D object detection track. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()