Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning (1904.07615v2)

Abstract: We show that denoising of 3D point clouds can be learned unsupervised, directly from noisy 3D point cloud data only. This is achieved by extending recent ideas from learning of unsupervised image denoisers to unstructured 3D point clouds. Unsupervised image denoisers operate under the assumption that a noisy pixel observation is a random realization of a distribution around a clean pixel value, which allows appropriate learning on this distribution to eventually converge to the correct value. Regrettably, this assumption is not valid for unstructured points: 3D point clouds are subject to total noise, i. e., deviations in all coordinates, with no reliable pixel grid. Thus, an observation can be the realization of an entire manifold of clean 3D points, which makes a na\"ive extension of unsupervised image denoisers to 3D point clouds impractical. Overcoming this, we introduce a spatial prior term, that steers converges to the unique closest out of the many possible modes on a manifold. Our results demonstrate unsupervised denoising performance similar to that of supervised learning with clean data when given enough training examples - whereby we do not need any pairs of noisy and clean training data.

• The paper introduces an unsupervised denoising method that cleans 3D point clouds using only noisy data and spatial priors.
• It employs a spatial prior term and appearance cues to distinguish clean points and prevent over-smoothing of sharp edges.
• Empirical results demonstrate competitive performance with supervised methods, enabling scalable processing of real-world scans.

Overview of Unsupervised Learning for 3D Point Cloud Denoising

The paper "Total Denoising: Unsupervised Learning of 3D Point Cloud Cleaning" presents a novel methodology for denoising 3D point clouds via unsupervised learning. Unlike conventional approaches that necessitate clean and noisy data pairs, this paper accomplishes effective denoising using only noisy data, leveraging advancements in unsupervised image denoising and adapting these to the complexities of 3D point clouds.

Methodological Innovations

Key to this approach is addressing the distinct challenges posed by total noise in 3D point clouds, where noise affects both spatial coordinates and attributes, such as color, without the regularity found in image data. The authors introduce a spatial prior term to guide convergence toward a probabilistically unique closest mode within the manifold of potential clean point locations. This spatial context, optionally augmented by appearance information, enables the network to effectively distinguish and reconstruct clean point data from noisy observations.

Performance and Implications

The evaluation demonstrates that this unsupervised denoising approach achieves performance comparable to supervised methods when provided sufficient noisy examples. Strong empirical results show that the proposed method can often perform equally well or better than supervised techniques, particularly when the scale of training data is considered. The use of spatial and appearance priors significantly enhances the network’s ability to retain sharp edges and prevent over-smoothing, a common issue in point cloud denoising.

Practical and Theoretical Contributions

The practical implications of this research are substantial, given the abundance of noisy 3D point cloud data compared to clean data, which is limited due to the manual nature of CAD modeling. This unsupervised technique could allow for rapid, large-scale processing of real-world scanned data, supporting applications across urban mapping, architecture, and digital content creation.

From a theoretical standpoint, the findings suggest possible extensions of unsupervised learning frameworks to other domains marked by high-dimensional, unstructured noise. The successful incorporation of spatial and color priors offers a promising avenue to improve the robustness of models against diverse types of noise.

Future Directions

Speculated future directions include adapting the method to integrate color and position denoising concurrently, further exploring denoising in more severely corrupted datasets, and extending application capabilities to point cloud segments from real-world scanning technology. The ability to scale efficiently with large datasets while maintaining high fidelity of details presents promising potential for accelerating advancements in AI-driven 3D content processing.

In summary, this paper contributes a significant advance in the unsupervised learning landscape, specifically tailored to the nuanced challenges of 3D point cloud denoising. This work not only demonstrates the feasibility of unsupervised techniques in complex settings but also underscores the importance of spatial and appearance priors in improving denoising outcomes amidst total noise conditions.