SemanticBEVFusion: Rethink LiDAR-Camera Fusion in Unified Bird's-Eye View Representation for 3D Object Detection

• URL: http://arxiv.org/abs/2212.04675v1
• Date: Fri, 9 Dec 2022 05:48:58 GMT
• Title: SemanticBEVFusion: Rethink LiDAR-Camera Fusion in Unified Bird's-Eye View Representation for 3D Object Detection
• Authors: Qi Jiang, Hao Sun, Xi Zhang
• Abstract summary: LiDAR and camera are two essential sensors for 3D object detection in autonomous driving. Recent methods focus on point-level fusion which paints the LiDAR point cloud with camera features in the perspective view. We present SemanticBEVFusion to deeply fuse camera features with LiDAR features in a unified BEV representation.
• Score: 14.706717531900708
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LiDAR and camera are two essential sensors for 3D object detection in autonomous driving. LiDAR provides accurate and reliable 3D geometry information while the camera provides rich texture with color. Despite the increasing popularity of fusing these two complementary sensors, the challenge remains in how to effectively fuse 3D LiDAR point cloud with 2D camera images. Recent methods focus on point-level fusion which paints the LiDAR point cloud with camera features in the perspective view or bird's-eye view (BEV)-level fusion which unifies multi-modality features in the BEV representation. In this paper, we rethink these previous fusion strategies and analyze their information loss and influences on geometric and semantic features. We present SemanticBEVFusion to deeply fuse camera features with LiDAR features in a unified BEV representation while maintaining per-modality strengths for 3D object detection. Our method achieves state-of-the-art performance on the large-scale nuScenes dataset, especially for challenging distant objects. The code will be made publicly available.

Related papers

• SimpleBEV: Improved LiDAR-Camera Fusion Architecture for 3D Object Detection [15.551625571158056]
  We propose a LiDAR-camera fusion framework, named SimpleBEV, for accurate 3D object detection. Our method achieves 77.6% NDS accuracy on the nuScenes dataset, showcasing superior performance in the 3D object detection track.
  arXiv  Detail & Related papers  (2024-11-08T02:51:39Z)
• VFMM3D: Releasing the Potential of Image by Vision Foundation Model for Monocular 3D Object Detection [80.62052650370416]
  monocular 3D object detection holds significant importance across various applications, including autonomous driving and robotics. In this paper, we present VFMM3D, an innovative framework that leverages the capabilities of Vision Foundation Models (VFMs) to accurately transform single-view images into LiDAR point cloud representations.
  arXiv  Detail & Related papers  (2024-04-15T03:12:12Z)
• MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection [89.26380781863665]
  Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images into 3D space for fusion. We propose a novel framework that focuses on the multi-scale progressive interaction of the multi-granularity LiDAR and camera features.
  arXiv  Detail & Related papers  (2022-09-07T12:29:29Z)
• Bridging the View Disparity of Radar and Camera Features for Multi-modal Fusion 3D Object Detection [6.959556180268547]
  This paper focuses on how to utilize millimeter-wave (MMW) radar and camera sensor fusion for 3D object detection. A novel method which realizes the feature-level fusion under bird-eye view (BEV) for a better feature representation is proposed.
  arXiv  Detail & Related papers  (2022-08-25T13:21:37Z)
• Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection [58.81316192862618]
  Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR. fusing these two modalities can significantly boost the performance of 3D perception models. We benchmark the state-of-the-art fusion methods for the first time.
  arXiv  Detail & Related papers  (2022-05-30T09:35:37Z)
• TransFusion: Robust LiDAR-Camera Fusion for 3D Object Detection with Transformers [49.689566246504356]
  We propose TransFusion, a robust solution to LiDAR-camera fusion with a soft-association mechanism to handle inferior image conditions. TransFusion achieves state-of-the-art performance on large-scale datasets. We extend the proposed method to the 3D tracking task and achieve the 1st place in the leaderboard of nuScenes tracking.
  arXiv  Detail & Related papers  (2022-03-22T07:15:13Z)
• DeepFusion: Lidar-Camera Deep Fusion for Multi-Modal 3D Object Detection [83.18142309597984]
  Lidars and cameras are critical sensors that provide complementary information for 3D detection in autonomous driving. We develop a family of generic multi-modal 3D detection models named DeepFusion, which is more accurate than previous methods.
  arXiv  Detail & Related papers  (2022-03-15T18:46:06Z)
• RoIFusion: 3D Object Detection from LiDAR and Vision [7.878027048763662]
  We propose a novel fusion algorithm by projecting a set of 3D Region of Interests (RoIs) from the point clouds to the 2D RoIs of the corresponding the images. Our approach achieves state-of-the-art performance on the KITTI 3D object detection challenging benchmark.
  arXiv  Detail & Related papers  (2020-09-09T20:23:27Z)
• Cross-Modality 3D Object Detection [63.29935886648709]
  We present a novel two-stage multi-modal fusion network for 3D object detection. The whole architecture facilitates two-stage fusion. Our experiments on the KITTI dataset show that the proposed multi-stage fusion helps the network to learn better representations.
  arXiv  Detail & Related papers  (2020-08-16T11:01:20Z)
• 3D-CVF: Generating Joint Camera and LiDAR Features Using Cross-View Spatial Feature Fusion for 3D Object Detection [10.507404260449333]
  We propose a new architecture for fusing camera and LiDAR sensors for 3D object detection. The proposed 3D-CVF achieves state-of-the-art performance in the KITTI benchmark.
  arXiv  Detail & Related papers  (2020-04-27T08:34:46Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.