MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection

• URL: http://arxiv.org/abs/2209.03102v1
• Date: Wed, 7 Sep 2022 12:29:29 GMT
• Title: MSMDFusion: Fusing LiDAR and Camera at Multiple Scales with Multi-Depth Seeds for 3D Object Detection
• Authors: Yang Jiao, Zequn Jie, Shaoxiang Chen, Jingjing Chen, Xiaolin Wei, Lin Ma, Yu-Gang Jiang
• Abstract summary: 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.
• Score: 89.26380781863665
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Fusing LiDAR and camera information is essential for achieving accurate and reliable 3D object detection in autonomous driving systems. However, this is challenging due to the difficulty of combining multi-granularity geometric and semantic features from two drastically different modalities. Recent approaches aim at exploring the semantic densities of camera features through lifting points in 2D camera images (referred to as seeds) into 3D space for fusion, and they can be roughly divided into 1) early fusion of raw points that aims at augmenting the 3D point cloud at the early input stage, and 2) late fusion of BEV (bird-eye view) maps that merges LiDAR and camera BEV features before the detection head. While both have their merits in enhancing the representation power of the combined features, this single-level fusion strategy is a suboptimal solution to the aforementioned challenge. Their major drawbacks are the inability to interact the multi-granularity semantic features from two distinct modalities sufficiently. To this end, we propose a novel framework that focuses on the multi-scale progressive interaction of the multi-granularity LiDAR and camera features. Our proposed method, abbreviated as MDMSFusion, achieves state-of-the-art results in 3D object detection, with 69.1 mAP and 71.8 NDS on nuScenes validation set, and 70.8 mAP and 73.2 NDS on nuScenes test set, which rank 1st and 2nd respectively among single-model non-ensemble approaches by the time of submission.

Related papers

• mmFUSION: Multimodal Fusion for 3D Objects Detection [18.401155770778757]
  Multi-sensor fusion is essential for accurate 3D object detection in self-driving systems. In this paper, we propose a new intermediate-level multi-modal fusion approach to overcome these challenges. The code with the mmdetection3D project plugin will be publicly available soon.
  arXiv  Detail & Related papers  (2023-11-07T15:11:27Z)
• Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection [11.575945934519442]
  LiDAR and camera fusion techniques are promising for achieving 3D object detection in autonomous driving. Most multi-modal 3D object detection frameworks integrate semantic knowledge from 2D images into 3D LiDAR point clouds. We propose a general multi-modal fusion framework Multi-Sem Fusion (MSF) to fuse the semantic information from both the 2D image and 3D points scene parsing results.
  arXiv  Detail & Related papers  (2022-12-10T10:54:41Z)
• SemanticBEVFusion: Rethink LiDAR-Camera Fusion in Unified Bird's-Eye View Representation for 3D Object Detection [14.706717531900708]
  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.
  arXiv  Detail & Related papers  (2022-12-09T05:48:58Z)
• FusionRCNN: LiDAR-Camera Fusion for Two-stage 3D Object Detection [11.962073589763676]
  Existing 3D detectors significantly improve the accuracy by adopting a two-stage paradigm. The sparsity of point clouds, especially for the points far away, makes it difficult for the LiDAR-only refinement module to accurately recognize and locate objects. We propose a novel multi-modality two-stage approach named FusionRCNN, which effectively and efficiently fuses point clouds and camera images in the Regions of Interest(RoI) FusionRCNN significantly improves the strong SECOND baseline by 6.14% mAP on baseline, and outperforms competing two-stage approaches.
  arXiv  Detail & Related papers  (2022-09-22T02:07:25Z)
• 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)
• 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)
• EPNet++: Cascade Bi-directional Fusion for Multi-Modal 3D Object Detection [56.03081616213012]
  We propose EPNet++ for multi-modal 3D object detection by introducing a novel Cascade Bi-directional Fusion(CB-Fusion) module. The proposed CB-Fusion module boosts the plentiful semantic information of point features with the image features in a cascade bi-directional interaction fusion manner. The experiment results on the KITTI, JRDB and SUN-RGBD datasets demonstrate the superiority of EPNet++ over the state-of-the-art methods.
  arXiv  Detail & Related papers  (2021-12-21T10:48:34Z)
• SO-Pose: Exploiting Self-Occlusion for Direct 6D Pose Estimation [98.83762558394345]
  SO-Pose is a framework for regressing all 6 degrees-of-freedom (6DoF) for the object pose in a cluttered environment from a single RGB image. We introduce a novel reasoning about self-occlusion, in order to establish a two-layer representation for 3D objects. Cross-layer consistencies that align correspondences, self-occlusion and 6D pose, we can further improve accuracy and robustness.
  arXiv  Detail & Related papers  (2021-08-18T19:49:29Z)
• LIF-Seg: LiDAR and Camera Image Fusion for 3D LiDAR Semantic Segmentation [78.74202673902303]
  We propose a coarse-tofine LiDAR and camera fusion-based network (termed as LIF-Seg) for LiDAR segmentation. The proposed method fully utilizes the contextual information of images and introduces a simple but effective early-fusion strategy. The cooperation of these two components leads to the success of the effective camera-LiDAR fusion.
  arXiv  Detail & Related papers  (2021-08-17T08:53:11Z)
• 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)

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.