Source-linked AI summary
Adaptive Graph Convolution for Point Cloud Analysis
Haoran Zhou, Yidan Feng, Mingsheng Fang, Mingqiang Wei, Jing Qin, Tong Lu
TL;DR
Point-cloud convolutions struggle because fixed kernels treat feature correspondences between irregular, semantically different points indistinguishably. AdaptConv generates pair-specific kernels from learned features within the convolution, and the paper reports state-of-the-art classification and segmentation performance across benchmark datasets. Its feature-based design can substantially increase computational cost when high-dimensional features are used.
Problem
Fixed graph-convolution kernels apply identically across neighbors, limiting representation of differing feature correspondences, particularly near salient regions such as corners and edges.
Method
AdaptConv dynamically generates a kernel for each point pair from their learned features and applies it to the pair's spatial relations inside graph convolution.
Results
AdaptConv achieves state-of-the-art performance on point-cloud classification and segmentation benchmarks and outperforms fixed-kernel graph-convolution alternatives.
Takeaways & Limitations
AdaptConv can be integrated into existing graph CNNs by replacing fixed kernels with adaptive kernels to improve point-cloud analysis performance.
Takeaways & Limitations
Using high-dimensional features for adaptive convolution can cause extremely high computational cost because the network expands heavily.
Abstract
from arXiv · showhide
Convolution on 3D point clouds that generalized from 2D grid-like domains is widely researched yet far from perfect. The standard convolution characterises feature correspondences indistinguishably among 3D points, presenting an intrinsic limitation of poor distinctive feature learning. In this paper, we propose Adaptive Graph Convolution (AdaptConv) which generates adaptive kernels for points according to their dynamically learned features. Compared with using a fixed/isotropic kernel, AdaptConv improves the flexibility of point cloud convolutions, effectively and precisely capturing the diverse relations between points from different semantic parts. Unlike popular attentional weight schemes, the proposed AdaptConv implements the adaptiveness inside the convolution operation instead of simply assigning different weights to the neighboring points. Extensive qualitative and quantitative evaluations show that our method outperforms state-of-the-art point cloud classification and segmentation approaches on several benchmark datasets. Our code is available at https://github.com/hrzhou2/AdaptConv-master.
1. Introduction
Point clouds are unstructured and unordered, making direct convolution difficult. AdaptConv addresses fixed-kernel limitations by generating pair-specific kernels from learned point features inside the graph convolution.
- 3D point clouds are unstructured and unordered, unlike the regular grids used by conventional 2D convolutions.This irregularity complicates direct generalization of CNN operations to point-cloud data.
- Standard graph convolutions apply shared fixed kernels to all neighboring point pairs, ignoring differences in feature correspondences.This can weaken geometric representations, especially for points from different semantic parts.
- Attention-based methods assign different neighbor weights but still operate on features produced by fixed-kernel convolutions.Their adaptiveness changes feature importance rather than the convolution kernel itself.
- AdaptConv generates kernels adaptively from learned point features and uses them inside the graph convolution.The method establishes pairwise relationships according to learned features instead of relying on predefined weights.
- The paper reports state-of-the-art performance for point-cloud classification and segmentation on several benchmark datasets.The proposed operator is presented as a flexible alternative to isotropic graph-convolution kernels.
2. Related Work
Related work includes voxel, point-based, and graph-based approaches for learning from irregular point clouds. AdaptConv differs by making the convolution kernel unique to each point pair rather than only weighting neighboring features.
- Voxelization-based and multi-view methods: Voxelization and projection methods enable conventional CNNs but can lose information and incur substantial computational cost.Octree-like structures reduce empty-voxel computation, while multi-view methods use projected representations.
- Point-based methods: Point-based methods process raw points directly, with PointNet providing permutation invariance but limited local geometric modeling.Later methods introduce local feature-learning mechanisms using transformations, polynomial kernels, spatial coordinates, or explicit kernel points.
- Graph-based methods: Graph-based methods model points as nodes and establish edges from spatial or feature relationships to capture local geometric structures.Existing graph approaches include nearest-neighbor feature graphs, distance-based kernels, Gaussian mixtures, and attention weights.
- Convolution on point clouds: AdaptConv generates a convolution kernel unique to each connected point pair, distinguishing it from other graph convolutions.Its adaptive-kernel design is illustrated as part of the point-pair convolution process.
- Convolution on point clouds: Prior dynamic-weight methods approximate weight functions, whereas AdaptConv makes the kernel itself adaptive within the convolution.This places adaptiveness in the convolution operation rather than only in a subsequent weighting function.
3. Method
AdaptConv builds point-cloud convolutions around kernels generated from learned feature relationships, then combines them with spatial relations and neighborhood aggregation. Separate architectures adapt this layer to segmentation and classification settings.
- Adaptive graph convolution: AdaptConv constructs a graph from each point’s k-nearest neighbors, including a self-loop, and maps D-dimensional inputs to M-dimensional features.The layer preserves the number of points while using local graph structure.
- Adaptive graph convolution: Unlike fixed kernels that treat all neighbors identically, AdaptConv dynamically generates pair-specific kernels from the features of the central and neighboring points.This targets differing feature correspondences, especially near salient geometric regions such as corners and edges.
- Adaptive graph convolution: For each output channel, a multilayer perceptron maps the concatenated feature input [f_i, f_j − f_i] to an adaptive kernel.The mapping captures both the central-point feature and the feature difference to its neighbor.
- Adaptive graph convolution: The adaptive kernel is combined with the corresponding spatial relation [x_i, x_j − x_i] through an inner product and nonlinear activation to form edge features.Stacking the channel-wise outputs yields an M-dimensional edge feature for each connected point pair.
- Network architecture: The segmentation architecture uses graph pooling and interpolation hierarchically, whereas the classification architecture updates graph neighborhoods dynamically according to feature similarity without pooling or interpolation.The segmentation encoder includes five convolution layers, with its last layer using standard graph convolution.
- Adaptive graph convolution: Central-point features are obtained by channel-wise max-pooling over neighboring edge features, while the overall convolution weights comprise the channel-specific feature-mapping functions.This aggregation completes the local adaptive convolution.
4. Evaluation
AdaptConv is evaluated on classification, part segmentation, indoor semantic segmentation, ablations, and efficiency. Across these settings, it achieves strong benchmark performance, with adaptive kernels outperforming fixed-kernel alternatives while retaining a relatively small model size.
- Classification: On ModelNet40, AdaptConv achieves the best reported classification scores using only point coordinates and approximately 1k input points.The evaluation reports mean class accuracy and overall accuracy.
- Part segmentation: On ShapeNetPart, AdaptConv achieves state-of-the-art part-segmentation performance under mean class IoU and mean instance IoU evaluation.The dataset contains 50 annotated parts across 16 shape categories.
- Indoor scene segmentation: On S3DIS Area 5, AdaptConv outperforms state-of-the-art methods in most categories and detects less obvious edges such as pictures and wall boards.The evaluation reports mIoU, mAcc, OA, and per-class IoU; qualitative results visualize labelled points mapped to RGB colors.
- Ablation studies: Replacing AdaptConv with attention-based or standard graph convolutions yields worse results, supporting the benefit of adaptive kernels over fixed-kernel graph convolutions.The ablations replace AdaptConv layers while keeping the remaining architecture and feature inputs consistent.
- Robustness and design choices: Using spatial coordinates as adaptive-kernel inputs is recommended, while reducing neighborhood size lowers cost but can degrade performance through a smaller receptive field.The study reports that k=5 still produces promising results, whereas larger neighborhoods can dilute local information at a given point density.
- Efficiency: AdaptConv reaches 93.4% overall accuracy on ModelNet40 with a relatively small model, outperforming DGCNN while remaining efficient.The comparison reports parameter counts and corresponding classification results.
5. Visualization and learned features
The visualization experiment examines whether AdaptConv distinguishes points with similar spatial inputs through learned feature relations. Selected target points from different object parts are compared with other points in feature space across intermediate network layers.
- Experiment setup: The experiment evaluates AdaptConv’s feature relations in intermediate network layers using ShapeNetPart segmentation models.Two target points from different object parts are selected, and Euclidean distances to other points in feature space are computed.
- Visualization: Points with similar learned features are visualized by coloring them red according to their feature-space distances from each target point.The visualization uses Euclidean distance in the learned feature space.
6. Conclusion
The paper concludes that AdaptConv dynamically generates kernels from point features, producing more flexible geometric representations than fixed kernels. It outperforms state-of-the-art methods across several point cloud analysis benchmarks and can be integrated into existing graph CNNs by replacing their kernels.
- Conclusion: AdaptConv dynamically generates adaptive convolution kernels from point features instead of using fixed kernels that capture point correspondences indistinguishably.The method is designed to produce more flexible learned features for geometric structures.
- Conclusion: AdaptConv-based end-to-end networks outperform state-of-the-art point cloud analysis methods on several public datasets.The conclusion reports results across several point cloud analysis tasks and benchmarks.
- Conclusion: Existing graph CNNs can incorporate AdaptConv by replacing their fixed kernels with adaptive kernels.The paper presents this integration as a simple way to improve existing graph CNNs.