PRNet: Self-Supervised Learning for Partial-to-Partial Registration (1910.12240v2)

Abstract: We present a simple, flexible, and general framework titled Partial Registration Network (PRNet), for partial-to-partial point cloud registration. Inspired by recently-proposed learning-based methods for registration, we use deep networks to tackle non-convexity of the alignment and partial correspondence problems. While previous learning-based methods assume the entire shape is visible, PRNet is suitable for partial-to-partial registration, outperforming PointNetLK, DCP, and non-learning methods on synthetic data. PRNet is self-supervised, jointly learning an appropriate geometric representation, a keypoint detector that finds points in common between partial views, and keypoint-to-keypoint correspondences. We show PRNet predicts keypoints and correspondences consistently across views and objects. Furthermore, the learned representation is transferable to classification.

• The paper introduces PRNet, a deep learning framework that iteratively refines partial-to-partial point cloud registration using a self-supervised, actor-critic approach.
• It leverages DGCNNs, Transformers, and Gumbel-Softmax sampling to efficiently extract geometric features and keypoint correspondences, outperforming traditional ICP methods.
• Empirical results on synthetic and real datasets verify improved registration accuracy and generalizability, highlighting potential extensions to SLAM and medical imaging.

Self-Supervised Learning for Partial-to-Partial Registration: Insights on PRNet

The paper in discussion introduces a framework known as the Partial Registration Network (PRNet), specifically designed to address the challenge of partial-to-partial point cloud registration. This task involves aligning two sets of spatial points, where only parts of the complete data are visible in each set, a scenario particularly relevant to applications in computer vision and robotics.

Framework and Method

PRNet builds on the strengths of deep learning frameworks by tackling the inherent non-convexity issues of alignment and addressing partial correspondence problems through the use of a self-supervised learning paradigm. The authors present a simple yet effective architecture that iteratively refines registration accuracy, diverging from traditional approaches such as Iterative Closest Point (ICP) which are computationally intensive and often inflexible due to their parameter-heavy nature.

Key Components:

• Deep Learning Architecture: The network leverages Dynamic Graph Convolutional Neural Networks (DGCNNs) and Transformers to extract and utilize geometric features effectively, introducing co-contextual information into the registration process.
• Iterative Alignment: Unlike past approaches that offer one-shot solutions, PRNet incorporates a sequential decision-making process, iteratively updating the alignment to produce superior registration results.
• Keypoint Detection: PRNet uniquely identifies keypoints using the $L^2$ norms of features derived from point cloud embeddings, enhancing the effectiveness of recognizing mutual geometrical structures in partial scans.
• Gumbel-Softmax Sampling: An innovative use of Gumbel-Softmax provides a near-differentiable method to sample sharp keypoint correspondences, thus balancing sharpness with smooth backpropagation benefits.
• Actor-Critic Architecture: The framework employs a sub-network predicting temperature parameters for the Gumbel-Softmax operation, conceptualizing this within an actor-critic learning paradigm, ensuring flexibility over varying data conditions.

Numerical Results and Implications

The empirical evaluations—conducted on synthetic datasets like ModelNet40 and ShapeNetCore, as well as on real data like the Stanford Bunny—highlight PRNet's superior performance across multiple metrics including Mean Squared Error (MSE), Root Mean Squared Error (RMSE), Mean Absolute Error (MAE), and $R^2$ for rotation and translation estimation tasks. The paper makes a compelling case for the method’s transferability by illustrating how representations learned for registration can also be applied to classification tasks, achieving promising accuracy.

Observations:

• PRNet consistently outperformed traditional techniques like ICP and recent learning-based models in various noise and occlusion scenarios.
• The network exhibits strong generalization capabilities across unseen categories, a critical aspect in many practical applications.
• The learned representations show potential for broader usage, possibly enhancing tasks like keypoint detection and correspondence prediction, crucial in 3D shape analysis.

Future Prospects

The paper suggests several paths for extending PRNet’s utility, pointing to its potential for incorporation within SLAM or structure-from-motion pipelines, and applications in medical imaging. Furthermore, it hints at enhancing the model’s scalability to process large-scale point clouds, as frequently encountered in LiDAR and modern 3D scanning technologies.

PRNet represents a significant step towards achieving robust partial-to-partial registration using machine learning, and its contribution extends beyond immediate applications, offering an adaptable framework poised for integration with future advancements in AI and robotics.