Source-linked AI summary
Patch-based Progressive 3D Point Set Upsampling
Wang Yifan, Shihao Wu, Hui Huang, Daniel Cohen-Or, Olga Sorkine-Hornung
TL;DR
Sparse, noisy, and irregular point sets require specialized upsampling because image-space super-resolution techniques do not transfer straightforwardly. The paper introduces an end-to-end progressive cascade of patch-based subnetworks, each targeting a different detail level, with architectural improvements for efficient feature processing and inter-level propagation. Experiments show robustness to noise and sparse inputs, favorable quantitative comparisons with state-of-the-art methods, and recovery of fine-grained geometric details.
Problem
3D point set upsampling is challenging because point sets are unstructured, irregular, sparse, noisy, and incomplete, while adapting image super-resolution methods is not straightforward.
Method
The method progressively trains a cascade of patch-based upsampling subnetworks, with adaptive receptive-field scopes and dense feature extraction, code-based expansion, and bilateral inter-level interpolation.
Results
The model is robust under noise and sparse inputs, compares favorably with state-of-the-art methods across quantitative measures, and restores fine-grained geometric details.
Takeaways & Limitations
Progressive, adaptive patch-based processing enables end-to-end training on high-resolution point sets while revealing detailed geometric structures from sparse and noisy inputs.
Takeaways & Limitations
The choice of input patch size is not critical for upsampling quality in the reported experiments, but small patches can restrict context and large patches can increase complexity and training difficulty.
Abstract
from arXiv · showhide
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.
1. Introduction
Point set upsampling adapts ideas from image super-resolution to sparse, noisy, irregular 3D data. The paper proposes a progressive patch-based network with architectural improvements that restores fine-grained details and performs robustly on difficult inputs.
- Image super-resolution, especially multi-step methods, motivates developing analogous upsampling methods for 3D point sets.
- Unlike images, point sets are unstructured, irregular, and commonly sparse, noisy, and incomplete, making direct adaptation of image methods difficult.
- The proposed network decomposes a 16× upsampling task into four 2× subnetworks, each specializing in a different detail level.
- All subnetworks operate on patches with adaptive input sizes, avoiding exponential point growth while enabling end-to-end training for large ratios and dense outputs.
- Dense point-wise connections, code assignment for feature expansion, and bilateral feature interpolation improve performance and parameter efficiency.
- The model is robust to noise and sparse inputs, compares favorably with state-of-the-art methods quantitatively, and restores fine-grained geometric details.
2. Related work
Prior point set upsampling methods include optimization-based approaches using geometric priors and deep networks based on PointNet-style processing. Existing methods face limitations related to sharp features, sparse inputs, or object-level processing.
- Optimization-based approaches: Optimization-based methods use geometric assumptions and operators such as Voronoi insertion, smooth MLS surfaces, anisotropic LOP, and edge-aware upsampling.
- Deep learning approaches: PointNet and PointNet++ address irregular point sets through shared MLPs for point features and symmetric functions such as max pooling for global extraction.
- Deep learning approaches: PointNet-based upsampling networks generalize across shape categories, but object-level training limits their application to low-resolution inputs.
- Multiscale skip connections in deep learning: Progressive skip connections in image super-resolution improve convergence and preservation of detail levels, motivating analogous multiscale designs for point processing.
3. Method
The method progressively upsamples point-set patches across multiple detail levels, combining adaptive patch processing with dense and inter-level feature connections. Its units extract, expand, and propagate features while progressively training the cascade end-to-end.
- Multi-step progressive upsampling: The network recursively upsamples point sets through multiple 2× steps, with each subnet targeting a different level of detail.Patch-based processing supports large upsampling ratios and dense outputs while avoiding exponential point growth.
- Multi-step progressive upsampling: Adaptive patch sizes match the receptive-field scope at each stage, avoiding unnecessary processing of all points as density increases.Fixed kNN neighborhoods yield narrower receptive fields on denser point sets, while per-point neighborhood computation is expensive.
- Multi-step progressive upsampling: Progressive training first activates a new unit with earlier units fixed, then jointly trains all units to reduce destructive gradient turbulence.For L levels, the cascade uses 2L −1 training stages, with two stages for every unit except the first.
- Upsampling network unit: Each upsampling unit extracts deep features, expands their number, and compresses them into d-dimensional output coordinates.The unit operates on a patch from Pℓ−1 and produces the next-level point set Pℓ.
- Feature extraction: Dense connections dynamically group points by feature similarity, refine grouped features with densely connected MLPs, and aggregate them using order-invariant maxpooling.Connections within and between dense blocks reuse information without point-set subsampling.
- Inter-level feature propagation: Inter-level bilateral feature interpolation bridges receptive fields across upsampling units, while residual links propagate coarse-scale context into finer-structure restoration.Experiments report residual links as more memory-efficient, stable, and accurate than dense links for inter-level propagation.
4. Results
The evaluation compares the proposed network with state-of-the-art methods and isolates the contributions of its progressive, patch-based design. Results show improved reconstruction under sparse, noisy, and real-scan conditions, with stronger detail preservation and fewer artifacts.
- Quantitative and qualitative comparison: The experiments compare the method quantitatively and qualitatively with state-of-the-art point upsampling approaches using Chamfer, Hausdorff, and point-to-surface distances.Comparisons use MNIST-CP, Sketchfab, and ModelNet10, with 16× upsampling from sparse and dense inputs.
- Quantitative and qualitative comparison: The proposed method outperforms EAR, PU-Net, and EC-Net by a large quantitative margin and produces less noisy outputs with more details.The comparison covers 625-point and 5000-point inputs on Sketchfab and 625-point inputs on ModelNet10.
- Ablation study: Multi-stage architecture, end-to-end training, adaptive patch extraction, and the proposed feature modules progressively improve local geometry and reduce artifacts.Ablations report benefits from dense feature extraction, feature expansion, and inter-level skip-connections, with feature extraction and expansion also reducing parameters.
- Ablation study: Adaptive patch extraction further refines local geometry by matching the input patch’s spatial span to the scope of the receptive field.Figure 7 compares direct, iterative, separately trained, progressively trained, and patch-based progressively trained variants.
- Robustness: The model is more robust than PU-Net to noise from 0% to 2% and sparsity from randomly removing 10% to 50% of input points.These stress tests are conducted on MNIST-CP datasets.
- Real-world data: On real scans, WLOP preprocessing followed by the network yields dense and clean outputs from noisy, outlier-ridden, and sparse point sets.The inputs come from a hand-held Intel RealSense SR300 scanner.
5. Conclusion
The proposed progressive point set upsampling network specializes in different detail levels while revealing geometric structures from sparse and noisy inputs. Its adaptive patch-based design and architectural enhancements support end-to-end training and strong comparative performance.
- The network progressively specializes in different levels of geometric detail to reveal structures from sparse and noisy point-set inputs.
- The stress test varies noise from 0 to 2% and input sparsity from 50 to 25 points while using a model trained with 50 points and 0.25% Gaussian noise.
- Adaptive patch-based processing reduces spatial span as receptive fields shrink, enabling end-to-end training on high-resolution point sets.
- Dense feature connections, code assignment, and bilateral feature interpolation provide architectural improvements for feature extraction, expansion, and inter-step propagation.