PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection

• URL: http://arxiv.org/abs/2408.14600v1
• Date: Mon, 26 Aug 2024 19:43:01 GMT
• Title: PVAFN: Point-Voxel Attention Fusion Network with Multi-Pooling Enhancing for 3D Object Detection
• Authors: Yidi Li, Jiahao Wen, Bin Ren, Wenhao Li, Zhenhuan Xu, Hao Guo, Hong Liu, Nicu Sebe,
• Abstract summary: integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. We propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN) PVAFN uses a multi-pooling strategy to integrate both multi-scale and region-specific information effectively.
• Score: 59.355022416218624
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The integration of point and voxel representations is becoming more common in LiDAR-based 3D object detection. However, this combination often struggles with capturing semantic information effectively. Moreover, relying solely on point features within regions of interest can lead to information loss and limitations in local feature representation. To tackle these challenges, we propose a novel two-stage 3D object detector, called Point-Voxel Attention Fusion Network (PVAFN). PVAFN leverages an attention mechanism to improve multi-modal feature fusion during the feature extraction phase. In the refinement stage, it utilizes a multi-pooling strategy to integrate both multi-scale and region-specific information effectively. The point-voxel attention mechanism adaptively combines point cloud and voxel-based Bird's-Eye-View (BEV) features, resulting in richer object representations that help to reduce false detections. Additionally, a multi-pooling enhancement module is introduced to boost the model's perception capabilities. This module employs cluster pooling and pyramid pooling techniques to efficiently capture key geometric details and fine-grained shape structures, thereby enhancing the integration of local and global features. Extensive experiments on the KITTI and Waymo datasets demonstrate that the proposed PVAFN achieves competitive performance. The code and models will be available.

Related papers

• Multi-scale Feature Fusion with Point Pyramid for 3D Object Detection [18.41721888099563]
  This paper proposes the Point Pyramid RCNN (POP-RCNN), a feature pyramid-based framework for 3D object detection on point clouds. The proposed method can be applied to a variety of existing frameworks to increase feature richness, especially for long-distance detection.
  arXiv  Detail & Related papers  (2024-09-06T20:13:14Z)
• PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest [65.48057241587398]
  PoIFusion is a framework to fuse information of RGB images and LiDAR point clouds at the points of interest (PoIs) Our approach maintains the view of each modality and obtains multi-modal features by computation-friendly projection and computation. We conducted extensive experiments on nuScenes and Argoverse2 datasets to evaluate our approach.
  arXiv  Detail & Related papers  (2024-03-14T09:28:12Z)
• MLF-DET: Multi-Level Fusion for Cross-Modal 3D Object Detection [54.52102265418295]
  We propose a novel and effective Multi-Level Fusion network, named as MLF-DET, for high-performance cross-modal 3D object DETection. For the feature-level fusion, we present the Multi-scale Voxel Image fusion (MVI) module, which densely aligns multi-scale voxel features with image features. For the decision-level fusion, we propose the lightweight Feature-cued Confidence Rectification (FCR) module, which exploits image semantics to rectify the confidence of detection candidates.
  arXiv  Detail & Related papers  (2023-07-18T11:26:02Z)
• PV-RCNN++: Semantical Point-Voxel Feature Interaction for 3D Object Detection [22.6659359032306]
  This paper proposes a novel object detection network by semantical point-voxel feature interaction, dubbed PV-RCNN++. Experiments on the KITTI dataset show that PV-RCNN++ achieves 81.60$%$, 40.18$%$, 68.21$%$ 3D mAP on Car, Pedestrian, and Cyclist, achieving comparable or even better performance to the state-of-the-arts.
  arXiv  Detail & Related papers  (2022-08-29T08:14:00Z)
• AGO-Net: Association-Guided 3D Point Cloud Object Detection Network [86.10213302724085]
  We propose a novel 3D detection framework that associates intact features for objects via domain adaptation. We achieve new state-of-the-art performance on the KITTI 3D detection benchmark in both accuracy and speed.
  arXiv  Detail & Related papers  (2022-08-24T16:54:38Z)
• LATFormer: Locality-Aware Point-View Fusion Transformer for 3D Shape Recognition [38.540048855119004]
  We propose a novel Locality-Aware Point-View Fusion Transformer (LATFormer) for 3D shape retrieval and classification. The core component of LATFormer is a module named Locality-Aware Fusion (LAF) which integrates the local features of correlated regions across the two modalities. In our LATFormer, we utilize the LAF module to fuse the multi-scale features of the two modalities both bidirectionally and hierarchically to obtain more informative features.
  arXiv  Detail & Related papers  (2021-09-03T03:23:27Z)
• MBDF-Net: Multi-Branch Deep Fusion Network for 3D Object Detection [17.295359521427073]
  We propose a Multi-Branch Deep Fusion Network (MBDF-Net) for 3D object detection. In the first stage, our multi-branch feature extraction network utilizes Adaptive Attention Fusion modules to produce cross-modal fusion features from single-modal semantic features. In the second stage, we use a region of interest (RoI) -pooled fusion module to generate enhanced local features for refinement.
  arXiv  Detail & Related papers  (2021-08-29T15:40:15Z)
• PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [57.49788100647103]
  LiDAR-based 3D object detection is an important task for autonomous driving. Current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions.
  arXiv  Detail & Related papers  (2020-12-18T18:06:43Z)
• Multi-View Adaptive Fusion Network for 3D Object Detection [14.506796247331584]
  3D object detection based on LiDAR-camera fusion is becoming an emerging research theme for autonomous driving. We propose a single-stage multi-view fusion framework that takes LiDAR bird's-eye view, LiDAR range view and camera view images as inputs for 3D object detection. We design an end-to-end learnable network named MVAF-Net to integrate these two components.
  arXiv  Detail & Related papers  (2020-11-02T00:06:01Z)
• 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.