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PRA-Net: Point Relation-Aware Network for 3D Point Cloud Analysis

Silin Cheng, Xiwu Chen, Xinwei He, Zhe Liu, Xiang Bai

arXiv:2112.04903v1cs.CV

TL;DR

PRA-Net addresses the underexplored joint use of intra-region contexts and inter-region relations in 3D point-cloud representation. It combines adaptive ISL and IRL modules, and experiments across multiple 3D benchmarks demonstrate effectiveness and generality.

  • Problem

    Existing point-cloud representation methods do not fully emphasize unifying intra-region contexts with inter-region relations.

  • Method

    PRA-Net combines ISL, which adaptively integrates local structure into point features, with IRL, which partitions regions and uses representative points to model inter-region relations.

  • Results

    Experiments on four public benchmarks covering classification, keypoint estimation, and part segmentation demonstrate PRA-Net’s effectiveness and generality.

  • Takeaways & Limitations

    Combining intra-region contexts with inter-region relations provides the basis for PRA-Net’s more discriminative point-cloud representations.

Abstract

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Learning intra-region contexts and inter-region relations are two effective strategies to strengthen feature representations for point cloud analysis. However, unifying the two strategies for point cloud representation is not fully emphasized in existing methods. To this end, we propose a novel framework named Point Relation-Aware Network (PRA-Net), which is composed of an Intra-region Structure Learning (ISL) module and an Inter-region Relation Learning (IRL) module. The ISL module can dynamically integrate the local structural information into the point features, while the IRL module captures inter-region relations adaptively and efficiently via a differentiable region partition scheme and a representative point-based strategy. Extensive experiments on several 3D benchmarks covering shape classification, keypoint estimation, and part segmentation have verified the effectiveness and the generalization ability of PRA-Net. Code will be available at https://github.com/XiwuChen/PRA-Net .

I. INTRODUCTION

PRA-Net addresses the need to jointly model intra-region contexts and inter-region relations in 3D point clouds. Its ISL and IRL modules form an end-to-end framework evaluated across classification, keypoint estimation, and part segmentation benchmarks.

  • PRA-Net jointly exploits intra-region contexts and inter-region relations to improve point-cloud feature representations.
  • ISL adaptively incorporates local structural information into each point feature, producing more discriminative intra-region contextual information.
  • IRL dynamically partitions point clouds, samples representative proxies, and models inter-region relations efficiently.
  • The framework combines ISL and IRL in an end-to-end architecture evaluated on object classification, keypoint estimation, and part segmentation benchmarks.

II. RELATED WORK

Prior point-cloud methods capture local structure or inter-region relations, but existing approaches leave limitations in geometric modeling, adaptivity, efficiency, or joint representation learning. PRA-Net is positioned as an adaptive approach that incorporates local structure and explores inter-region relations.

  • PRA-Net: The ISL design combines neighbor-based local structure, self-based global shape information, and dynamic feature aggregation.
  • Point-based method: Point-based methods directly process unordered clouds, but earlier approaches do not fully exploit intra-region geometric relationships.
  • Point-based method: Existing local-structure methods aggregate neighboring features, whereas PRA-Net adaptively incorporates local structure into each point feature.
  • Inter-region relations: Some methods model inter-region relations through structural, spatial, or global-attention mechanisms, but global attention can introduce computational and representational redundancy.

III. PROPOSED METHOD

PRA-Net combines ISL for intra-region structure learning with IRL for inter-region relation modeling. ISL fuses neighborhood-derived local structure and global shape information, while IRL partitions points into regions for relation learning.

  • PRA-Net contains ISL for enhancing point features with contextual regions and IRL for modeling relations across local regions.
  • Intra-region Structure Learning: ISL computes local structural features from differences between each point and its nearest neighbors, then aggregates them with max-pooling.
  • Intra-region Structure Learning: ISL also extracts global shape information from point features and dynamically fuses local and global representations.
  • Inter-region Relation Learning: The IRL pipeline includes dynamic partition, representative-point sampling, inter-region relation modeling, and feature interpolation.

B. Inter-region Relation Learning

The IRL module adaptively partitions point clouds into local regions, samples representative points, and uses self-attention to model inter-region relations efficiently. A differentiable partition and sparse representative-point graph reduce the cost of relation modeling.

  • Region partition: IRL dynamically selects centroids, constructs k-nearest-neighbor regions, and uses these regions to model inter-region relations.Centroids are selected through importance scores and k-NN regions are formed around them.
  • Region partition: The dilated top-S strategy balances point importance with spatial diffusion by sampling centroids with stride N/S instead of selecting only the highest-scoring points.This reduces the risk of selecting many nearby centroids while overlooking informative points elsewhere.
  • Inter-region relation learning: Representative-point attention replaces dense all-to-all inter-region connections with sampled proxies from each region.The naive approach connects points across regions, whereas the improved method applies self-attention to representative points.
  • Efficiency: The representative-point method reduces graph edges from S(S −1)k2 to S(S −1)m, lowering computational complexity relative to the naive graph.The reduction follows from using m representative points per region instead of all k points.
  • Feature interpolation: IRL restores its region-level output to the original point space through inverse Euclidean distance-weighted interpolation and adds it to the original embeddings.Interpolation uses the 3-nearest neighbors before the residual addition.

C. Network Architecture Details

PRA-Net uses task-specific network branches for classification, keypoint saliency estimation, and part segmentation, while all branches combine ISL and IRL modules.

  • Classification: The classification network alternates local-feature extraction and IRL processing, then aggregates representations through shortcut concatenation and pooling before prediction.It uses four LFE modules and three IRL modules, with max-pooling and mean-pooling outputs concatenated for the final representation.
  • Task-specific architectures: The classification and keypoint estimation tasks share an architecture family, while part segmentation uses a separate task-specific architecture.Figure 5 distinguishes the classification/keypoint branches from the segmentation branch.
  • Shared components: All three architectures contain both the ISL and IRL modules.The modules jointly provide intra-region and inter-region feature processing across tasks.
  • Keypoint saliency estimation: The keypoint saliency network follows the classification design and forms pointwise features by concatenating outputs from its local-feature and IRL modules.Its branch is shown separately from classification but uses a similar architecture.

D. Network Configuration Details

The paper specifies ISL and IRL configurations by task, defining neighborhood, region, and representative-point parameters and listing their layer sequences.

  • Module notation: ISL(ˆk, [l1;...;ld]) denotes an ISL module with ˆk nearest neighbors and d shared MLP layers of widths li.The notation describes the local neighborhood size and MLP architecture.
  • Module notation: IRL(S, k, m) denotes an IRL module with S regions, k total points per local region, and m representative points per region.These parameters specify the region partition and representative-point sampling configuration.
  • Classification and keypoint estimation: Classification and keypoint estimation use three IRL stages with configurations IRL(256, 4, 4), IRL(128, 8, 4), and IRL(64, 16, 4).Their ISL widths progress from 64 to 128 to 256 across the listed sequence.
  • Part segmentation: Part segmentation uses three IRL stages configured as IRL(128, 16, 8), IRL(256, 16, 8), and IRL(128, 32, 16).Its ISL modules use width sequences [64, 64, 64], [128, 128, 128], and [256, 256, 256].

IV. EXPERIMENTS

The experiments evaluate PRA-Net across shape classification, keypoint saliency estimation, and part segmentation to assess its performance across multiple 3D point-cloud analysis tasks.

  • Evaluation scope: Experiments cover shape classification, keypoint saliency estimation, and part segmentation.These tasks provide the stated evaluation scope for PRA-Net.

A. Point Cloud Classification

PRA-Net is evaluated on synthetic ModelNet40 and real-world ScanObjectNN classification datasets, using 1,024 input points and k-NN representative-point sampling. On ModelNet40, it reports strong overall and class accuracy.

  • Datasets and setup: PRA-Net evaluates classification on ModelNet40 and ScanObjectNN, including the hardest ScanObjectNN PB T50 RS variant.ModelNet40 contains 12,311 objects in 40 categories; ScanObjectNN contains 15,000 objects in 15 classes.
  • Datasets and setup: Using 1,024 points per object, PRA-Net applies k-NN representative-point sampling and standard augmentation during ModelNet40 training.Training uses random scaling from 0.66 to 1.33 and translation from -0.2 to 0.2.
  • Synthetic object classification: 90.5% mAcc and 93.2% OA are achieved on ModelNet40, outperforming almost all compared state-of-the-art methods using 1,024 input points.The reported metrics are mean class accuracy and overall accuracy.

B. Keypoint Saliency Estimation

PRA-Net is evaluated for keypoint saliency estimation on KeypointNet, where it achieves the best reported mIoU and mAP among the compared methods. The evaluation uses a distance threshold of 0.01.

  • Results: The compared classification-results table is identified as Table I, while the keypoint metrics are separately reported in Tables II and III.Tables II and III correspond to mIoU and mAP, respectively.
  • Dataset and evaluation: KeypointNet contains 8,329 mesh models and 83,231 keypoints across 16 categories, with each model downsampled to 2,048 points.The train, validation, and test split ratio is 7:1:2.
  • Dataset and evaluation: The evaluation reports mIoU and mAP using a distance threshold of 0.01.The network uses the classification architecture and Adam optimization with an initial learning rate of 0.001.
  • Results: 28.6% mIoU and 43.2% mAP are achieved, improving over DGCNN by 7.8% and 14.4%, respectively.The paper reports these as the best performance and states that qualitative results accurately identify annotated keypoints.

C. Part Segmentation

PRA-Net is evaluated on ShapeNet Part for semantic part segmentation across 16 shape categories and 50 part classes. It achieves competitive instance and class mIoU while producing faithful qualitative segmentations.

  • Dataset and setup: ShapeNet Part contains 16,880 models across 16 shape categories and 50 part classes, with 14,006 training and 2,874 testing models.Each object is annotated with one shape category and 2 to 6 part classes.
  • Dataset and setup: The part segmentation network uses ˆk = 32 in each ISL module and applies scheduled learning-rate decay during training.The learning rate decays by 0.5 every 30 epochs.
  • Results: 86.3% instance mIoU and 83.7% class mIoU are reported on ShapeNet Part.PRA-Net outperforms PointNet++, DGCNN, and A-CNN, while exceeding RSCNN on instance mIoU but slightly trailing it on class mIoU.
  • Results: The qualitative results show that PRA-Net can faithfully segment semantic parts of each object.The qualitative results are presented in Fig. 6.

D. Discussions

The discussion studies component effectiveness, region partitioning, representative-point sampling, group sizes, and IRL efficiency. Results favor learned, distributed sampling with moderate representative and region counts, while representative points substantially reduce inference cost.

  • Component effectiveness: Ablation experiments evaluate the effects of ISL and IRL on shape classification and keypoint saliency estimation.The study compares component configurations using baselines that primarily exploit intra-region contextual information.
  • Sampling strategies: k-NN representative-point sampling achieves 81.0% OA, while either too few or too many representative points degrades performance; the best setting is m = 4.The comparison considers random and k-NN-based sampling strategies.
  • Region partitioning: Dilated top-S sampling distributes points more evenly than top-S sampling and better describes object geometric topology.The discussion contrasts this learned region-partition strategy with task-agnostic FPS.
  • Region counts: The best region configurations are S = (256, 128, 64) across the three IRL layers, while overly small or large group numbers harm performance.The paper attributes the decline to insufficient description with too few regions and redundant information with too many.
  • Relation visualization: IRL attention captures inter-region relations including spatial symmetry and semantic dependencies among different parts.The paper visualizes influential points and corresponding segmentation ground truth in Fig. 9.

V. CONCLUSION AND FUTURE WORK

PRA-Net combines intra-region contexts and inter-region relations in an end-to-end architecture for 3D point cloud analysis. Its ISL and IRL modules produce discriminative point-cloud representations, with effectiveness and generality demonstrated on four public benchmarks.

  • PRA-Net combines intra-region contexts and inter-region relations in an end-to-end architecture for 3D point cloud analysis.
  • The ISL module dynamically integrates local structural information into point features to extract intra-region contexts.
  • The IRL module adaptively samples representative points from local regions to model inter-region relations.
  • Combining ISL and IRL yields a principled framework for learning highly discriminative point-cloud representations.
  • Comprehensive experiments on four public benchmark datasets demonstrate the effectiveness and generality of PRA-Net.
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