Patch-based Progressive 3D Point Set Upsampling (1811.11286v3)

Abstract: We present a detail-driven deep neural network for point set upsampling. A high-resolution point set is essential for point-based rendering and surface reconstruction. Inspired by the recent success of neural image super-resolution techniques, we progressively train a cascade of patch-based upsampling networks on different levels of detail end-to-end. We propose a series of architectural design contributions that lead to a substantial performance boost. The effect of each technical contribution is demonstrated in an ablation study. Qualitative and quantitative experiments show that our method significantly outperforms the state-of-the-art learning-based and optimazation-based approaches, both in terms of handling low-resolution inputs and revealing high-fidelity details.

• The paper introduces a novel progressive, patch-based deep learning framework for upsampling sparse 3D point sets into dense outputs with high geometric detail.
• The method employs a multi-step, end-to-end architecture that progressively refines details using adaptively sized patches and dense feature connections.
• Experimental results demonstrate that the network quantitatively and qualitatively outperforms state-of-the-art methods on various datasets and conditions, achieving improved fidelity and parameter efficiency.

Overview of Patch-based Progressive 3D Point Set Upsampling

The paper introduces a novel neural network framework for the upsampling of 3D point sets, addressing the inherent challenges posed by the unstructured nature of point data and its regular usage by consumer-level scanning devices. The network is designed to upsample sparse point sets into dense outputs with high geometric detail using a progressive, patch-based deep learning approach.

Methodology

The proposed approach leverages a series of neural network design innovations to efficiently transform low-resolution point sets into detailed high-resolution outputs. The network improves upon existing techniques by adopting a multi-step, end-to-end patch-based learning scheme. This architecture enables the model to iteratively refine geometric details at progressively finer levels.

1. Multi-step Upsampling: The architecture breaks down the upsampling task into multiple stages, each trained to focus on specified levels of detail. The model is trained in a progressive manner, helping maintain the geometric integrity across varying detail levels.
2. Patch-based Processing: To handle large upsampling ratios without an exponential growth in data and to facilitate efficient training, the network processes data in patches. The sizes of these patches dynamically adapt based on the detail level being processed.
3. Feature Extraction and Expansion: Dense connections are employed within the model to extract rich features efficiently, with a novel method of feature expansion via code assignment introduced to manage feature multiplicity and distribution effectively.
4. End-to-End Training: By training the units progressively in a lockstep manner, the paper proposes that subsequent units can mitigate earlier point placement errors, enhancing the overall fidelity and sharpness of the output.

Experimental Results

The method outperforms state-of-the-art alternatives, both quantitatively and qualitatively, in handling different scales of detail, input sparsity, and noisy data. These tests were conducted on several datasets, including Sketchfab and ModelNet10. Key performance metrics such as Chamfer Distance and Hausdorff Distance highlight the network's superiority in maintaining closeness to the ground truth even under challenging conditions.

Implications and Future Work

The network demonstrates marked improvements in point cloud upsampling, achieving significant parameter efficiency and expanded functionality. Future directions may involve extending the capabilities of this model into areas like real-time processing or adapting it for cross-sensor fidelity enhancements.

This research not only extends the understanding of multi-stage learning approaches in the domain of 3D data processing but also sets the groundwork for further innovations in handling complex, unstructured geometric data with high precision and efficiency. As this method continues to evolve, its foundational principles can underpin more advanced architectures in 3D reconstruction and beyond.