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PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks
Guocheng Qian, Abdulellah Abualshour, Guohao Li, Ali Thabet, Bernard Ghanem
TL;DR
Point cloud upsampling must recover dense, clean geometry from sparse and noisy sensor data, while existing modules can miss neighborhood detail or replicate input patches. The paper introduces GCN-based NodeShuffle and multi-scale Inception DenseGCN, combining them in PU-GCN. PU-GCN outperforms state-of-the-art methods across two datasets while using fewer parameters and more efficient inference.
Problem
Existing point upsampling modules either ignore neighborhood information or generate patches resembling the input, limiting local detail.
Method
The paper combines the GCN-based NodeShuffle upsampling module with the multi-scale Inception DenseGCN feature extractor in PU-GCN.
Results
PU-GCN outperforms state-of-the-art methods on PU1K and another dataset while requiring fewer parameters and more efficient inference.
Takeaways & Limitations
NodeShuffle improves existing upsampling pipelines, while Inception DenseGCN provides further gains in the final upsampled point clouds.
Takeaways & Limitations
The evaluation uses a ×4 upsampling rate and does not experiment with a large upsampling factor.
Abstract
from arXiv · showhide
The effectiveness of learning-based point cloud upsampling pipelines heavily relies on the upsampling modules and feature extractors used therein. For the point upsampling module, we propose a novel model called NodeShuffle, which uses a Graph Convolutional Network (GCN) to better encode local point information from point neighborhoods. NodeShuffle is versatile and can be incorporated into any point cloud upsampling pipeline. Extensive experiments show how NodeShuffle consistently improves state-of-the-art upsampling methods. For feature extraction, we also propose a new multi-scale point feature extractor, called Inception DenseGCN. By aggregating features at multiple scales, this feature extractor enables further performance gain in the final upsampled point clouds. We combine Inception DenseGCN with NodeShuffle into a new point upsampling pipeline called PU-GCN. PU-GCN sets new state-of-art performance with much fewer parameters and more efficient inference.
1. Introduction
Point cloud upsampling addresses sparse, noisy sensor outputs, but existing modules often ignore neighborhood information or replicate input patches, limiting local detail. The paper proposes NodeShuffle and Inception DenseGCN, combining them in PU-GCN for improved structure preservation and multi-scale detail.
- Sparse and noisy 3D sensor outputs motivate converting incomplete point clouds into dense, complete, and clean ones.
- PU-GCN combines NodeShuffle with Inception DenseGCN to improve upsampling quality and generate fine-grained structure details.Figure 1 illustrates details such as the faucet’s neck and ball shape.
- Existing upsampling modules either process points independently with multi-branch MLPs or duplicate input points, limiting local detail.
- NodeShuffle uses GCNs to encode neighborhood information and learn new points rather than merely replicating input parts.
- Inception DenseGCN uses a multi-path densely connected GCN architecture to extract multi-scale point features without the progressive cost of 3PU.
2. Related Work
Related work spans learning-based point cloud upsampling, shape completion, and graph convolutional networks. Existing methods include multi-scale and edge-aware approaches, while graph upsampling remains constrained in handling unordered, irregular data and arbitrary expansion ratios.
- Deep learning methods improve on optimization-based point cloud upsampling through data-driven learning and neural-network capacity.
- PU-Net learns multi-scale per-point features and expands point sets with multi-branch MLPs, but its initial downsampling causes resolution loss.
- Point cloud upsampling is closely related to 3D shape completion, with prior work using voxel, implicit, and point-based representations.
- GCN research has developed spectral, dynamic, residual, dense, and dilated graph-convolution techniques for non-Euclidean data.
- Graph U-Nets introduce graph unpooling, but it only restores the original graph structure and cannot upsample nodes by an arbitrary ratio.
3. Methodology
PU-GCN combines NodeShuffle, a GCN-based point upsampling module, with Inception DenseGCN, a multi-scale feature extractor, into a unified pipeline. NodeShuffle expands features and rearranges them into new points, while Inception DenseGCN aggregates information across receptive fields using dense, dilated graph convolutions and global pooling.
- NodeShuffle: NodeShuffle uses a GCN to expand node features from N × C to N × rC, then periodically shuffles them into rN × C features.The shuffle operation rearranges feature maps, with r denoting the upsampling ratio.
- NodeShuffle: NodeShuffle uses graph convolutions to encode point-neighborhood information and learn new points from latent features rather than duplicating or separately transforming input points.This design targets the locality limitations of multi-branch MLPs and duplicate-based upsampling methods.
- Inception DenseGCN: Inception DenseGCN integrates densely connected graph convolutions into an Inception-style extractor to encode the multi-scale nature of point clouds.Its parallel DenseGCN blocks use different dilation rates while sharing kernel size, producing different receptive fields.
- Inception DenseGCN: Inception DenseGCN combines parallel dilated DenseGCN blocks, global pooling, and skip connections, with bottleneck layers reducing computation.The blocks and global pooling target different receptive fields, and their outputs are concatenated with the input features.
- PU-GCN Pipeline: PU-GCN combines Inception DenseGCN, NodeShuffle, and a coordinate reconstructor to transform an N × 3 point cloud into an rN × 3 upsampled cloud.The feature extractor produces N × C features, NodeShuffle maps them to rN × C′, and the coordinate reconstructor generates coordinates.
4. Experiments
Experiments evaluate PU-GCN on PU-GAN’s dataset and the larger PU1K dataset using quantitative, qualitative, ablation, and noisy-input tests. PU-GCN outperforms the compared methods while preserving details, reducing outliers, and maintaining parameter and inference efficiency.
- Datasets: PU1K contains 1,147 3D models and is nearly 8 times larger than the largest publicly available point-cloud upsampling dataset.Its 1,020 training and 127 testing models include data from PU-GAN’s dataset and ShapeNetCore.
- Quantitative results: On PU-GAN’s dataset, PU-GCN improves over PU-Net and 3PU on all metrics and surpasses PU-GAN in CD and HD.PU-GCN also uses about 10% of PU-GAN’s parameters and speeds inference by more than 40%.
- Quantitative results: On PU1K, PU-GCN clearly outperforms PU-Net and 3PU in CD, HD, and P2F on the more challenging dataset.PU-GAN is not compared on PU1K because its results could not be reproduced from the publicly available code.
- Qualitative results: Qualitative comparisons show that PU-GCN produces fewer outliers and preserves fine-grained structures better than competing methods on synthetic and real-scanned point clouds.Examples include intricate bird structures, statue details, separated clock structures, car windows, and motorcycle pedals.
- Ablation study: Ablations show that NodeShuffle improves PU-Net and 3PU with less parameters and negligible computational overhead, while Inception DenseGCN further improves intricate-structure preservation.The reported NodeShuffle latency overhead is at most 1 ms.
- Robustness: With additive Gaussian input noise, PU-GCN preserves fine-grained details with very few outliers in qualitative comparisons against PU-GAN.Both models were trained using the same point-cloud perturbation augmentation strategy.
5. Conclusion
The paper introduces NodeShuffle and Inception DenseGCN, combining them into PU-GCN for point cloud upsampling. Experiments report improved performance across datasets, with fewer parameters, more efficient inference, and higher quality on real-scanned point clouds.
- PU-GCN integrates the NodeShuffle upsampling module with the Inception DenseGCN multi-scale feature extractor.These components form the paper’s proposed point upsampling pipeline.
- NodeShuffle improves state-of-the-art upsampling pipelines when used in place of their original upsampling modules.
- Inception DenseGCN encodes multi-scale information for point cloud feature extraction.
- PU-GCN outperforms state-of-the-art methods on PU1K and another dataset while requiring fewer parameters and more efficient inference.
- PU-GCN produces higher-quality upsampling on real-scanned point clouds than other methods.