CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds (2012.15638v2)

Abstract: Motivated by the intuition that one can transform two aligned point clouds to each other more easily and meaningfully than a misaligned pair, we propose CorrNet3D -- the first unsupervised and end-to-end deep learning-based framework -- to drive the learning of dense correspondence between 3D shapes by means of deformation-like reconstruction to overcome the need for annotated data. Specifically, CorrNet3D consists of a deep feature embedding module and two novel modules called correspondence indicator and symmetric deformer. Feeding a pair of raw point clouds, our model first learns the pointwise features and passes them into the indicator to generate a learnable correspondence matrix used to permute the input pair. The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence. The extensive experiments on both synthetic and real-world datasets of rigid and non-rigid 3D shapes show our CorrNet3D outperforms state-of-the-art methods to a large extent, including those taking meshes as input. CorrNet3D is a flexible framework in that it can be easily adapted to supervised learning if annotated data are available. The source code and pre-trained model will be available at https://github.com/ZENGYIMING-EAMON/CorrNet3D.git.

• The paper introduces a deformation-driven deep learning framework that computes dense correspondences in 3D point clouds.
• It employs DGCNN-based feature embeddings and a novel DeSmooth module to refine similarity matrices without heavy supervision.
• Experimental evaluations show CorrNet3D outperforms state-of-the-art methods on rigid and non-rigid datasets, demonstrating robustness and scalability.

Unsupervised Dense Correspondence for 3D Point Clouds: An Examination of CorrNet3D

CorrNet3D introduces a significant advancement in the computation of dense correspondence between 3D point clouds through an unsupervised, end-to-end deep learning framework. The method departs from traditional approaches that rely heavily on annotated datasets or connectivity information, providing an innovative stance by leveraging deformation-driven methodologies for learning point correspondences. This paper presents a structured exploration of CorrNet3D, focusing on its theoretical underpinnings, implemented architecture, and practical implications.

Architecture and Methodology

CorrNet3D's architecture is built around three primary components: a feature embedding module, a correspondence indicator, and a symmetric deformer. The feature embedding module employs DGCNN, a well-regarded network architecture for point cloud processing, to extract high-dimensional features from the input point clouds. These features encapsulate local geometric structures which are critical for correspondence learning.

The novelty of CorrNet3D predominantly lies in the correspondence indicator and symmetric deformer. The correspondence indicator constructs a correspondence matrix by refining a similarity matrix obtained from feature distances. The method introduces a novel DeSmooth module which seeks to enforce a row-wise sparsity in the correspondence matrix, enhancing its alignment qualities. This approach bypasses the computational inefficiency intrinsic to Sinkhorn layers, offering a straightforward yet effective alternative.

The symmetric deformer capitalizes on the learned permutation matrix, transforming two aligned point clouds into each other. It embraces a deformation-like reconstruction paradigm, employing a shared multilayer perceptron (MLP) to drive the unsupervised learning method, indicating the innovative integration of geometry and machine learning principles in handling complex 3D shapes.

Experimental Evaluation

The performance evaluation of CorrNet3D highlights its effectiveness across various datasets comprising rigid and non-rigid shapes. When benchmarked against state-of-the-art methods such as DeepGFM and RPMNet, CorrNet3D showed superior ability in correctly establishing dense correspondences. Its performance on the SHREC dataset and real scanned 8iVFB dataset exemplifies its scalability and robustness, factors crucial for real-world scenarios involving complex structural deformations.

Furthermore, the research evaluates CorrNet3D under both supervised and unsupervised configurations, with the latter configuration demonstrating surprisingly robust performance even in the absence of ground-truth annotations—indicating the strength of the model's underlying assumptions and design.

Implications and Future Prospects

CorrNet3D paves the way for further developments in the utilization of deep learning methodologies for 3D data. Its success in unsupervised settings can alleviate the dependency on labeled datasets, which are cumbersome and challenging to create for 3D data. The deformation-driven approach presents a scalable framework adaptable to various domains such as AR/VR, autonomous navigation, and digital geometry processing.

The methodology ignites a potential research avenue focused on integrating more complex deformation models and extending to denser and more diverse datasets. Engaging with the dynamic quality of real-time 3D data while maintaining computational efficiency remains a high-value target for future explorations.

In conclusion, CorrNet3D demonstrates promising advancements in dense correspondence computation for 3D point clouds, positioning itself as a pivotal work within the domain of computer vision and AI. Its methodological innovations and practical efficacy spell likely avenues for future contributions that could revolutionize how 3D spatial data is utilized and understood.