Geometry-Aware 3D Salient Object Detection Network

• URL: http://arxiv.org/abs/2502.16488v1
• Date: Sun, 23 Feb 2025 08:02:34 GMT
• Title: Geometry-Aware 3D Salient Object Detection Network
• Authors: Chen Wang, Liyuan Zhang, Le Hui, Qi Liu, Yuchao Dai,
• Abstract summary: We propose a geometry-aware 3D salient object detection network that explicitly clusters points into superpoints.<n>Our method achieves new state-of-the-art performance on the PCSOD dataset.
• Score: 44.094231645179065
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud salient object detection has attracted the attention of researchers in recent years. Since existing works do not fully utilize the geometry context of 3D objects, blurry boundaries are generated when segmenting objects with complex backgrounds. In this paper, we propose a geometry-aware 3D salient object detection network that explicitly clusters points into superpoints to enhance the geometric boundaries of objects, thereby segmenting complete objects with clear boundaries. Specifically, we first propose a simple yet effective superpoint partition module to cluster points into superpoints. In order to improve the quality of superpoints, we present a point cloud class-agnostic loss to learn discriminative point features for clustering superpoints from the object. After obtaining superpoints, we then propose a geometry enhancement module that utilizes superpoint-point attention to aggregate geometric information into point features for predicting the salient map of the object with clear boundaries. Extensive experiments show that our method achieves new state-of-the-art performance on the PCSOD dataset.

Related papers

• 3D Part Segmentation via Geometric Aggregation of 2D Visual Features [57.20161517451834]
  Supervised 3D part segmentation models are tailored for a fixed set of objects and parts, limiting their transferability to open-set, real-world scenarios.<n>Recent works have explored vision-language models (VLMs) as a promising alternative, using multi-view rendering and textual prompting to identify object parts.<n>To address these limitations, we propose COPS, a COmprehensive model for Parts that blends semantics extracted from visual concepts and 3D geometry to effectively identify object parts.
  arXiv  Detail & Related papers  (2024-12-05T15:27:58Z)
• SPGroup3D: Superpoint Grouping Network for Indoor 3D Object Detection [23.208654655032955]
  Current 3D object detection methods for indoor scenes mainly follow the voting-and-grouping strategy to generate proposals. We propose a novel superpoint grouping network for indoor anchor-free one-stage 3D object detection. Experimental results demonstrate our method achieves state-of-the-art performance on ScanNet V2, SUN RGB-D, and S3DIS datasets.
  arXiv  Detail & Related papers  (2023-12-21T08:08:02Z)
• MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer [4.644319899528183]
  LiDAR point clouds can effectively depict the motion and posture of objects in three-dimensional space. In autonomous driving scenarios, the sparsity and hollowness of point clouds create some difficulties for voxel-based methods. We propose a two-stage 3D object detection framework, called MS23D.
  arXiv  Detail & Related papers  (2023-08-31T08:03:25Z)
• PSA-Det3D: Pillar Set Abstraction for 3D object Detection [14.788139868324155]
  We propose a pillar set abstraction (PSA) and foreground point compensation (FPC) to improve the detection performance for small object. The experiments on the KITTI 3D detection benchmark show that our proposed PSA-Det3D outperforms other algorithms with high accuracy for small object detection.
  arXiv  Detail & Related papers  (2022-10-20T03:05:34Z)
• RBGNet: Ray-based Grouping for 3D Object Detection [104.98776095895641]
  We propose the RBGNet framework, a voting-based 3D detector for accurate 3D object detection from point clouds. We propose a ray-based feature grouping module, which aggregates the point-wise features on object surfaces using a group of determined rays. Our model achieves state-of-the-art 3D detection performance on ScanNet V2 and SUN RGB-D with remarkable performance gains.
  arXiv  Detail & Related papers  (2022-04-05T14:42:57Z)
• SASA: Semantics-Augmented Set Abstraction for Point-based 3D Object Detection [78.90102636266276]
  We propose a novel set abstraction method named Semantics-Augmented Set Abstraction (SASA) Based on the estimated point-wise foreground scores, we then propose a semantics-guided point sampling algorithm to help retain more important foreground points during down-sampling. In practice, SASA shows to be effective in identifying valuable points related to foreground objects and improving feature learning for point-based 3D detection.
  arXiv  Detail & Related papers  (2022-01-06T08:54:47Z)
• Group-Free 3D Object Detection via Transformers [26.040378025818416]
  We present a simple yet effective method for directly detecting 3D objects from the 3D point cloud. Our method computes the feature of an object from all the points in the point cloud with the help of an attention mechanism in the Transformers citevaswaniattention. With few bells and whistles, the proposed method achieves state-of-the-art 3D object detection performance on two widely used benchmarks, ScanNet V2 and SUN RGB-D.
  arXiv  Detail & Related papers  (2021-04-01T17:59:36Z)
• Learning Geometry-Disentangled Representation for Complementary Understanding of 3D Object Point Cloud [50.56461318879761]
  We propose Geometry-Disentangled Attention Network (GDANet) for 3D image processing. GDANet disentangles point clouds into contour and flat part of 3D objects, respectively denoted by sharp and gentle variation components. Experiments on 3D object classification and segmentation benchmarks demonstrate that GDANet achieves the state-of-the-arts with fewer parameters.
  arXiv  Detail & Related papers  (2020-12-20T13:35:00Z)
• 3D Object Classification on Partial Point Clouds: A Practical Perspective [91.81377258830703]
  A point cloud is a popular shape representation adopted in 3D object classification. This paper introduces a practical setting to classify partial point clouds of object instances under any poses. A novel algorithm in an alignment-classification manner is proposed in this paper.
  arXiv  Detail & Related papers  (2020-12-18T04:00:56Z)

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.