Enhancing point cloud analysis via neighbor aggregation correction based on cross-stage structure correlation

• URL: http://arxiv.org/abs/2506.15160v1
• Date: Wed, 18 Jun 2025 06:08:17 GMT
• Title: Enhancing point cloud analysis via neighbor aggregation correction based on cross-stage structure correlation
• Authors: Jiaqi Shi, Jin Xiao, Xiaoguang Hu, Boyang Song, Hao Jiang, Tianyou Chen, Baochang Zhang,
• Abstract summary: Point cloud analysis is a cornerstone of many downstream tasks, among which aggregating local structures is the basis for understanding point cloud data.<n>We propose the Point Distribution Set Abstraction module (PDSA) that utilizes the correlation in the high-dimensional space to correct the feature distribution during aggregation.<n>PDSA distinguishes the point correlation based on a lightweight cross-stage structural descriptor, and enhances structural homogeneity.
• Score: 22.48120946682699
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Point cloud analysis is the cornerstone of many downstream tasks, among which aggregating local structures is the basis for understanding point cloud data. While numerous works aggregate neighbor using three-dimensional relative coordinates, there are irrelevant point interference and feature hierarchy gap problems due to the limitation of local coordinates. Although some works address this limitation by refining spatial description though explicit modeling of cross-stage structure, these enhancement methods based on direct geometric structure encoding have problems of high computational overhead and noise sensitivity. To overcome these problems, we propose the Point Distribution Set Abstraction module (PDSA) that utilizes the correlation in the high-dimensional space to correct the feature distribution during aggregation, which improves the computational efficiency and robustness. PDSA distinguishes the point correlation based on a lightweight cross-stage structural descriptor, and enhances structural homogeneity by reducing the variance of the neighbor feature matrix and increasing classes separability though long-distance modeling. Additionally, we introducing a key point mechanism to optimize the computational overhead. The experimental result on semantic segmentation and classification tasks based on different baselines verify the generalization of the method we proposed, and achieve significant performance improvement with less parameter cost. The corresponding ablation and visualization results demonstrate the effectiveness and rationality of our method. The code and training weight is available at: https://github.com/AGENT9717/PointDistribution

Related papers

• KAN or MLP? Point Cloud Shows the Way Forward [13.669234791655075]
  We propose PointKAN, which applies Kolmogorov-Arnold Learning Networks (KANs) to point cloud analysis tasks.<n>We show that PointKAN outperforms PointMLP on benchmark datasets such as ModelNet40, ScanNN, and ShapeNetPart.<n>This work highlights the potential of KANs-based architectures in 3D vision and opens new avenues for research in point cloud understanding.
  arXiv  Detail & Related papers  (2025-04-18T09:52:22Z)
• Efficient Learnable Collaborative Attention for Single Image Super-Resolution [18.955369476815136]
  Non-Local Attention (NLA) is a powerful technique for capturing long-range feature correlations in deep single image super-resolution (SR) We propose a novel Learnable Collaborative Attention (LCoA) that introduces inductive bias into non-local modeling. Our LCoA can reduce the non-local modeling time by about 83% in the inference stage.
  arXiv  Detail & Related papers  (2024-04-07T11:25:04Z)
• CPR++: Object Localization via Single Coarse Point Supervision [55.8671776333499]
  coarse point refinement (CPR) is first attempt to alleviate semantic variance from an algorithmic perspective. CPR reduces semantic variance by selecting a semantic centre point in a neighbourhood region to replace the initial annotated point. CPR++ can obtain scale information and further reduce the semantic variance in a global region.
  arXiv  Detail & Related papers  (2024-01-30T17:38:48Z)
• Efficient Semantic Matching with Hypercolumn Correlation [58.92933923647451]
  HCCNet is an efficient yet effective semantic matching method. It exploits the full potential of multi-scale correlation maps. It eschews the reliance on expensive match-wise relationship mining on the 4D correlation map.
  arXiv  Detail & Related papers  (2023-11-07T20:40:07Z)
• Decoupled Local Aggregation for Point Cloud Learning [12.810517967372043]
  We propose to decouple the explicit modelling of spatial relations from local aggregation. We present DeLA, a lightweight point network, where in each learning stage relative spatial encodings are first formed. DeLA achieves over 90% overall accuracy on ScanObjectNN and 74% mIoU on S3DIS Area 5.
  arXiv  Detail & Related papers  (2023-08-31T08:21:29Z)
• pCTFusion: Point Convolution-Transformer Fusion with Semantic Aware Loss for Outdoor LiDAR Point Cloud Segmentation [8.24822602555667]
  This study proposes a new architecture, pCTFusion, which combines kernel-based convolutions and self-attention mechanisms. The proposed architecture employs two types of self-attention mechanisms, local and global, based on the hierarchical positions of the encoder blocks. The results are particularly encouraging for minor classes, often misclassified due to class imbalance, lack of space, and neighbor-aware feature encoding.
  arXiv  Detail & Related papers  (2023-07-27T11:12:48Z)
• Far Away in the Deep Space: Dense Nearest-Neighbor-Based Out-of-Distribution Detection [33.78080060234557]
  Nearest-Neighbors approaches have been shown to work well in object-centric data domains. We show that nearest-neighbor approaches also yield state-of-the-art results on dense novelty detection in complex driving scenes.
  arXiv  Detail & Related papers  (2022-11-12T13:32:19Z)
• Dynamic Convolution for 3D Point Cloud Instance Segmentation [146.7971476424351]
  We propose an approach to instance segmentation from 3D point clouds based on dynamic convolution. We gather homogeneous points that have identical semantic categories and close votes for the geometric centroids. The proposed approach is proposal-free, and instead exploits a convolution process that adapts to the spatial and semantic characteristics of each instance.
  arXiv  Detail & Related papers  (2021-07-18T09:05:16Z)
• DyCo3D: Robust Instance Segmentation of 3D Point Clouds through Dynamic Convolution [136.7261709896713]
  We propose a data-driven approach that generates the appropriate convolution kernels to apply in response to the nature of the instances. The proposed method achieves promising results on both ScanetNetV2 and S3DIS. It also improves inference speed by more than 25% over the current state-of-the-art.
  arXiv  Detail & Related papers  (2020-11-26T14:56:57Z)
• Making Affine Correspondences Work in Camera Geometry Computation [62.7633180470428]
  Local features provide region-to-region rather than point-to-point correspondences. We propose guidelines for effective use of region-to-region matches in the course of a full model estimation pipeline. Experiments show that affine solvers can achieve accuracy comparable to point-based solvers at faster run-times.
  arXiv  Detail & Related papers  (2020-07-20T12:07:48Z)
• Augmented Parallel-Pyramid Net for Attention Guided Pose-Estimation [90.28365183660438]
  This paper proposes an augmented parallel-pyramid net with attention partial module and differentiable auto-data augmentation. We define a new pose search space where the sequences of data augmentations are formulated as a trainable and operational CNN component. Notably, our method achieves the top-1 accuracy on the challenging COCO keypoint benchmark and the state-of-the-art results on the MPII datasets.
  arXiv  Detail & Related papers  (2020-03-17T03:52:17Z)

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.