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Point2Sequence: Learning the Shape Representation of 3D Point Clouds with an Attention-based Sequence to Sequence Network
Xinhai Liu, Zhizhong Han, Yu-Shen Liu, Matthias Zwicker
TL;DR
Existing point-cloud methods struggle to capture fine-grained correlations among areas within local regions when contextual information is encoded explicitly. Point2Sequence uses attention-based RNN encoder-decoder aggregation of multi-scale area features, and reports competitive or outperforming results in shape classification and part segmentation.
Problem
Explicit or hand-crafted local-context encoding makes fine-grained correlations between different areas difficult to capture.
Method
Point2Sequence extracts features from multi-scale areas and implicitly aggregates them with an attention-based RNN encoder-decoder.
Results
Point2Sequence achieves competitive state-of-the-art performance in shape classification and part segmentation, with reported improvements over existing methods.
Takeaways & Limitations
The results support using attention-based sequence modeling to learn correlations among multi-scale areas in point-cloud local regions.
Abstract
from arXiv · showhide
Exploring contextual information in the local region is important for shape understanding and analysis. Existing studies often employ hand-crafted or explicit ways to encode contextual information of local regions. However, it is hard to capture fine-grained contextual information in hand-crafted or explicit manners, such as the correlation between different areas in a local region, which limits the discriminative ability of learned features. To resolve this issue, we propose a novel deep learning model for 3D point clouds, named Point2Sequence, to learn 3D shape features by capturing fine-grained contextual information in a novel implicit way. Point2Sequence employs a novel sequence learning model for point clouds to capture the correlations by aggregating multi-scale areas of each local region with attention. Specifically, Point2Sequence first learns the feature of each area scale in a local region. Then, it captures the correlation between area scales in the process of aggregating all area scales using a recurrent neural network (RNN) based encoder-decoder structure, where an attention mechanism is proposed to highlight the importance of different area scales. Experimental results show that Point2Sequence achieves state-of-the-art performance in shape classification and segmentation tasks.
Introduction
Point2Sequence targets the difficulty of capturing fine-grained contextual information and correlations among areas in local point-cloud regions. It uses attention-based sequence learning to improve feature discrimination for classification and segmentation.
- PointNet aggregates individual point features globally, limiting its ability to capture local-region context.
- Prior local-region methods commonly encode context through hand-crafted or explicit aggregation, making fine-grained area correlations difficult to capture.
- Point2Sequence implicitly captures correlations among local-region areas by aggregating multi-scale features with an RNN-based encoder-decoder and attention.
- Each local region is separated into area scales, whose features are extracted with a shared MLP before sequence aggregation.
- The authors report more discriminative point-cloud features than existing methods for shape classification and part segmentation.
Related Work
Related work processes point clouds through rasterization, structured spatial indexing, or direct point-set learning. Point2Sequence instead directly models correlations among multi-scale local areas with an attention-based sequence-to-sequence structure.
- Learning from point clouds by rasterization: Volumetric rasterization enables 3D convolutions but suffers from sparsity-related time and memory costs, especially for large point clouds.
- Learning from point clouds by rasterization: OctNet and Kd-Net use octrees or kd-trees to construct regular structures that facilitate deep-learning operations on point clouds.
- Learning from point clouds directly: Point2Sequence learns directly from point clouds without pre-alignment or voxelization.
- Learning from point clouds directly: PointNet processes points independently before global pooling, which limits local contextual modeling.
- Learning from point clouds directly: PointNet++, PointCNN, and SpiderCNN incorporate local neighborhoods through hierarchical grouping or graph-based edge operations.
- Correlation learning with RNN-based models: Point2Sequence uses an RNN-based sequence-to-sequence model with attention to aggregate multi-scale area features and learn their correlations.
The Point2Sequence Model
Point2Sequence represents each local region as a sequence of multi-scale area features, then uses an attention-based RNN encoder-decoder to aggregate their contextual correlations into local and global shape features.
- Multi-scale area establishment: The model samples local-region centroids, searches multi-scale nearest-neighbor areas, and groups their points for feature extraction.Farthest point sampling provides centroids, while kNN retrieves fixed-size neighborhoods that are less sensitive to sampling density.
- Task-specific outputs: Local-region features are aggregated into a 1024-dimensional global feature for shape classification or per-point part segmentation.Training minimizes error against shape class labels or per-point part labels; segmentation additionally derives per-point features from the global representation through duplication or interpolation.
- Area feature extraction: A shared MLP and max pooling convert each scale area into a D-dimensional feature, producing a T × D sequence for every local region.Area coordinates are expressed relative to the centroid, and centroid coordinates are combined with area features.
- Attention-based sequence aggregation: An RNN encoder integrates the multi-scale feature sequence so its final hidden state contains contextual information from the entire local region.The encoder processes one D-dimensional area feature at each of T steps, using a nonlinear unit such as LSTM or GRU.
- Attention-based sequence aggregation: Attention weights focus the decoder on important area scales by forming a context vector from the encoder’s hidden states.The attended decoder output is used as the feature for each local region.
- Attention-based sequence aggregation: A one-step RNN decoder transforms the encoder context into the local-region feature, without requiring a separate decoding target.The encoder’s final hidden state serves as the decoder target, and the decoder output becomes the local-region representation.
Experiments
Point2Sequence is evaluated through parameter ablations, shape classification on ModelNet benchmarks, and part segmentation on ShapeNet part.
- Experimental design: The experiments examine network settings, compare Point2Sequence with state-of-the-art methods, and evaluate classification and part segmentation.The study covers parameter effects, ModelNet10/40 shape classification, and ShapeNet part segmentation.
- Parameter ablation: M = 384 sampled points yields the highest ModelNet40 instance accuracy of 92.54%.Accuracy rises and then falls as M varies from 128 to 512.
- Parameter ablation: LSTM with hidden state dimension h = 128 is more suitable than the tested GRU and alternative hidden-state dimensions.Replacing LSTM with GRU gives 92.18%, while h = 64 and h = 256 give 92.46% and 92.18%, respectively.
- Parameter ablation: The attention-plus-encoder-decoder configuration outperforms removing attention, removing the decoder, concatenation, and max pooling for multi-scale feature aggregation.The comparison evaluates No Att, No Dec, No ED, concatenation, and max pooling variants.
- Parameter ablation: T = 2 reaches 92.62% accuracy, and configurations with more than one scale outperform T = 1.For T = 2, the scales contain 64 and 128 points.
- Shape classification: Point2Sequence achieves 92.6% instance accuracy on ModelNet40, exceeding PointNet++ by 1.9% and DGCNN by 0.2%.It also outperforms other compared methods in class-average and instance-average accuracies on ModelNet10 and ModelNet40.
- Part segmentation: Point2Sequence achieves the best mean instance IoU of 85.2% on ShapeNet part segmentation among the compared methods.The paper also reports comparable performance across many categories and visual results highly consistent with ground truth.
Conclusions
The paper proposes an attention-based sequence-to-sequence framework for point-cloud processing that models correlations among multi-scale local areas. Experiments report competitive performance with state-of-the-art methods in shape classification and part segmentation.
- Point2Sequence is a representation-learning framework for point-cloud shape classification and part segmentation.The framework uses an attention-based sequence-to-sequence model over multi-scale local areas.
- Its attention mechanism highlights important multi-scale areas while the sequence model learns correlations among areas within local regions.The model is designed to utilize a sequence of multi-scale areas for local-region representation.
- Experimental results show competitive performance with state-of-the-art methods in shape classification and part segmentation.