PU-GCN: Point Cloud Upsampling using Graph Convolutional Networks (1912.03264v3)

Abstract: 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.

• The paper introduces PU-GCN, a novel framework using Graph Convolutional Networks with NodeShuffle and Inception DenseGCN modules for efficient and high-quality point cloud upsampling.
• PU-GCN achieves state-of-the-art performance in point cloud upsampling with fewer model parameters and faster inference speed than previous methods.
• The authors also propose PU1K, a large-scale, diverse dataset designed to serve as a new challenging benchmark for point cloud upsampling algorithms.

PU-GCN: Point Cloud Upsampling Using Graph Convolutional Networks

The paper introduces a novel approach to the increasingly relevant task of point cloud upsampling by leveraging Graph Convolutional Networks (GCNs). Point clouds, a common representation for 3D data, are often prone to sparsity and noise due to limitations in current 3D sensing technologies such as LiDAR, which are integral to autonomous systems like robotics and self-driving cars. This research attempts to convert sparse point clouds into dense, high-resolution representations without the typical drawback in computational complexity and parameter counts associated with previous methods.

Proposed Method

The authors present PU-GCN, a pipeline that combines two key components: NodeShuffle, an upsampling module, and Inception DenseGCN, a feature extraction module.

• NodeShuffle: This new module utilizes the structure provided by GCNs to effectively encode and enrich local point neighborhood information, thus allowing for a more robust upsampling process when compared to traditional methods such as duplicate or MLP-based upsampling. NodeShuffle not only enhances the quality of upsampling but also enables using fewer model parameters with more efficient inference.
• Inception DenseGCN: Inspired by multi-scale feature extraction practices in image processing, this module aggregates features at multiple scales through dense connections and dilated convolutions within the GCN framework. It facilitates the retention of global and local context, critical for capturing the complex nature of 3D geometries.

This integration results in the PU-GCN pipeline that yields more effectively upsampled point clouds with fewer artifacts and more preservation of intricate details compared to existing methods.

Contributions and Results

The paper outlines three major contributions:

1. NodeShuffle Upsampling Module: The introduction of a GCN-based upsampling module that can be seamlessly integrated into existing pipelines to improve performance.
2. Inception DenseGCN Feature Extractor: A feature extraction block that encodes multi-scale information essential for high-quality upsampling, bundled into the PU-GCN pipeline.
3. Dataset with High Diversity: PU1K, a large-scale dataset for point cloud upsampling, is proposed, providing a challenging benchmark for current algorithms.

Experimental results, both quantitative and qualitative, show that PU-GCN sets a new standard of performance when compared to state-of-the-art techniques such as PU-Net, 3PU, and PU-GAN. Moreover, the reduction in the number of parameters and improved inference speed enhances its practicality for computationally constrained environments.

Implications and Future Directions

From a theoretical standpoint, the effective amalgamation of multi-scale feature extraction and graph-based convolution operations marks a significant enhancement in point cloud processing, reinforcing the role of GCNs in 3D data tasks. In practice, these advancements point to more reliable, accurate, and efficient solutions in applications demanding real-time processing of point clouds, such as autonomous driving and robotics.

Future developments might focus on further tuning of GCN architectures for extended upsampling factors or real-time processing adaptability on consumer-grade hardware. Additionally, expanding the approach to encompass additional supervisory signals or integrate more diverse datasets might offer more opportunities for generalizing the proposed methods.

In essence, this paper offers the research community a compelling framework demonstrating how GCNs can be intelligently employed to overcome limitations in current point cloud upsampling approaches. The introduction of PU1K also helps to push the boundaries for developing robust algorithms that can tackle diverse real-world scenarios.