Source-linked AI summary
Point Cloud Upsampling via Disentangled Refinement
Ruihui Li, Xianzhi Li, Pheng-Ann Heng, Chi-Wing Fu
TL;DR
Sparse, non-uniform, and noisy scans make it difficult to produce dense point clouds that are both surface-faithful and uniformly distributed. The paper disentangles these objectives into cascaded dense-generation and spatial-refinement stages, with local/global refinement and per-point adjustment. It reports superiority over state-of-the-art methods on synthetic and real-scanned inputs.
Problem
Point cloud upsampling must jointly produce dense, uniformly distributed, surface-faithful points from sparse, noisy inputs that may contain holes and miss fine structures.
Method
The framework cascades a dense generator that creates coarse dense points with a spatial refiner that adjusts their locations using local and global refinement and per-point offsets.
Results
The method outperforms other methods on both real-scanned and synthetic inputs.
Takeaways & Limitations
Disentangling generation and refinement lets each sub-network focus on its own objective while improving adaptability at larger upsampling rates.
Takeaways & Limitations
The framework remains generic, with future work needed on more comprehensive sub-network architectures, region-adaptive refinement, and edge awareness.
Abstract
from arXiv · showhide
Point clouds produced by 3D scanning are often sparse, non-uniform, and noisy. Recent upsampling approaches aim to generate a dense point set, while achieving both distribution uniformity and proximity-to-surface, and possibly amending small holes, all in a single network. After revisiting the task, we propose to disentangle the task based on its multi-objective nature and formulate two cascaded sub-networks, a dense generator and a spatial refiner. The dense generator infers a coarse but dense output that roughly describes the underlying surface, while the spatial refiner further fine-tunes the coarse output by adjusting the location of each point. Specifically, we design a pair of local and global refinement units in the spatial refiner to evolve a coarse feature map. Also, in the spatial refiner, we regress a per-point offset vector to further adjust the coarse outputs in fine-scale. Extensive qualitative and quantitative results on both synthetic and real-scanned datasets demonstrate the superiority of our method over the state-of-the-arts.
1. Introduction
Point cloud upsampling must simultaneously densify sparse scans, preserve surface proximity, improve uniformity, and potentially amend holes despite incomplete, noisy inputs. The paper addresses this multi-objective difficulty by cascading a dense generator with a spatial refiner, achieving improved results on real-scanned and synthetic inputs.
- 3D-scanned point clouds are often sparse, non-uniform, noisy, and affected by small surface holes, limiting downstream rendering and analysis.
- Upsampling must generate dense points that remain near the underlying surface and cover it uniformly despite limited information and missing fine structures.
- Existing learning-based methods expand points in feature space and use losses for uniformity and surface proximity, but single networks struggle to satisfy all requirements.
- The proposed framework separates coarse dense generation from spatial refinement, using cascaded sub-networks with distinct sub-goals.
- The dense generator and spatial refiner focus on separate objectives, while cascading lets them complement each other during learning.
2. Related Work
Point-cloud upsampling research has progressed from optimization methods using hand-crafted priors to deep networks that learn feature extraction and point expansion. Existing approaches nevertheless rely on a single network for multiple objectives, motivating the proposed disentangled formulation.
- Optimization-based upsampling: Optimization-based methods generate points using hand-crafted priors, including Voronoi vertices and locally optimal projection under the L1 norm.These methods include an early moving-least-squares surface construction and later point resampling based on an L1-norm projection operator.
- Deep learning-based upsampling: Deep learning has been applied broadly to point-cloud processing, including classification, detection, completion, and denoising.
- Deep learning-based upsampling: PU-Net learns multi-scale features and expands point sets through multi-branch convolution in feature space.It is described as the first deep-learning attempt for point-cloud upsampling.
- Deep learning-based upsampling: EC-Net targets edge-aware upsampling, while MPU progressively upsamples point patches across multiple steps.EC-Net is presented as enhancing surface reconstruction quality through edge-aware processing.
- Motivation: Prior methods use one network to pursue dense generation, surface faithfulness, uniformity, and hole amendment simultaneously.The paper responds by disentangling these objectives into two cascaded networks and reports substantial improvements over prior work.
3. Method
The method disentangles point-cloud upsampling into cascaded dense generation and spatial refinement, assigning coarse surface coverage to the first stage and uniformity and surface proximity to the second. The spatial refiner combines local and global feature processing, predicts per-point offsets, and is trained jointly with the generator.
- Overview: The framework first generates a coarse but dense point set, then refines it to improve surface proximity and distribution uniformity.The two cascaded sub-networks target separate sub-goals while complementing each other during learning.
- Spatial Refiner: The spatial refiner consumes Q′ and FE, combines local and global refinement outputs into FR, and predicts per-point offsets ∆Q for the final points Q.The output is formed as Q′ + ∆Q, adjusting each coarse point rather than directly regressing absolute refined coordinates.
- Dense Generator: The dense generator maps sparse P of N points to coarse Q′ of rN points by extracting features, expanding them with a regular 2D grid, and regressing coordinates.Feature expansion duplicates FP with r copies and concatenates the copies with a regular 2D grid before coordinate regression.
- Spatial Refiner: Local refinement encodes neighborhood geometry with KNN grouping and spatial weighting, while global refinement uses self-attention to model long-range dependencies across points.The local unit weights neighbor features before aggregation; the global unit concatenates FE and Q′ to preserve overall shape structure.
- Patch-based End-to-end Training: Patch-based training optimizes Chamfer-distance consistency for both Q′ and Q, with λ increasing during training to shift emphasis toward the spatial refiner.The objective encourages consistency between Q′ and the target ˆQ and between Q and ˆQ; a small early λ prioritizes generator training.
- Patch-based End-to-end Training: The method reports that repulsion loss contributes little because the disentangled refinement scheme already produces relatively uniform dense points without an explicit uniformity loss.This observation is based on the authors’ experiments combining the objective with repulsion loss.
4. Experiments
Experiments evaluate the method on synthetic and real-scanned data using standard distance metrics, visual comparisons, noise tests, and ablations. Across these evaluations, the method produces uniform, low-noise outputs, preserves fine structures, performs well at different upsampling rates and noise levels, and benefits from its major components.
- Experimental setup: Experiments use synthetic and real-scanned datasets, with CD, HD, and P2F as quantitative metrics; lower values indicate better performance.The comparison includes PU-Net, MPU, and PU-GAN under matched training settings.
- Real-scanned results: On real-scanned inputs, the method produces uniform dense surfaces and higher-quality reconstructed meshes than competing methods.Competitors tend to retain noise or produce non-uniform outputs, rough surfaces, and small holes.
- Synthetic results: On synthetic inputs, the method best preserves tiny local structures and uniform distribution, while achieving the lowest error-map errors and verified CD and HD values.Other methods introduce excessive noise, cluster points, or destroy small structures.
- Synthetic results: The method achieves the lowest values on all evaluation metrics at both tested upsampling rates, with advantages becoming more pronounced as the rate increases.Its number of learnable parameters does not increase with the upsampling rate, supporting scalability and adaptability.
- Noise robustness: Across increasing noise levels at r = 16, the method produces more uniform results without excessive noise and the lowest CD values by a significant margin.The noise robustness test uses Gaussian noise added to synthetic inputs.
- Ablation study: Ablations remove the spatial refiner, local or global refinement units, or offsets; the reported results show that each component contributes to overall performance.Each ablated network was retrained and evaluated on synthetic data, with visual results also provided.
5. Conclusion
The paper concludes that disentangling point cloud upsampling into dense generation and spatial refinement addresses its multiple objectives more effectively than a single-network design. The framework combines local and global refinement with per-point offsets, while future work targets broader architectures, adaptive refinement, and edge awareness.
- Conclusion: The framework separates upsampling into coarse dense generation followed by point-location refinement instead of meeting all goals in one network.The two stages target surface description first, then surface proximity and distribution uniformity.
- Conclusion: The spatial refiner uses local and global refinement units and regresses offset vectors to adjust coarse outputs at fine scale.These units evolve features using local and global geometric structures.
- Future work: Future work includes more comprehensive generator and refiner architectures, region-adaptive refinement, and edge-aware refinement for mesh reconstruction.The proposed region-adaptive design would focus refinement on non-uniform and noisy regions.