3DRegNet: A Deep Neural Network for 3D Point Registration (1904.01701v2)

Abstract: We present 3DRegNet, a novel deep learning architecture for the registration of 3D scans. Given a set of 3D point correspondences, we build a deep neural network to address the following two challenges: (i) classification of the point correspondences into inliers/outliers, and (ii) regression of the motion parameters that align the scans into a common reference frame. With regard to regression, we present two alternative approaches: (i) a Deep Neural Network (DNN) registration and (ii) a Procrustes approach using SVD to estimate the transformation. Our correspondence-based approach achieves a higher speedup compared to competing baselines. We further propose the use of a refinement network, which consists of a smaller 3DRegNet as a refinement to improve the accuracy of the registration. Extensive experiments on two challenging datasets demonstrate that we outperform other methods and achieve state-of-the-art results. The code is available.

• The paper introduces 3DRegNet, which classifies 3D point correspondences and regresses transformation parameters to achieve precise registration.
• It integrates deep neural networks with the Procrustes SVD approach, demonstrating superior accuracy over traditional methods.
• A refinement network further enhances performance, reducing errors and computational overhead in real-time applications like robotics and AR.

Overview of 3DRegNet: A Deep Neural Network for 3D Point Registration

This paper introduces 3DRegNet, an advanced deep learning framework specifically designed for the registration of 3D point clouds. In 3D point registration, aligning two or more datasets collected by different captures into a single coordinate system is crucial, especially in domains like robotics and computer vision. Addressing this problem, 3DRegNet offers a novel solution that combines deep learning techniques for efficient and effective scan alignment.

Key Contributions

1. Inliers/Outliers Classification and Motion Parameters Regression: 3DRegNet distinguishes itself by not only classifying 3D point correspondences into inliers and outliers but also by regressing the motion parameters necessary to align the scans into a common reference frame. The dual focus on classification and regression underscores the network's comprehensive ability to simultaneously handle point correspondences and misalignments.
2. Application of Deep Neural Network (DNN) and Procrustes Method: The architecture utilizes a Deep Neural Network to directly compute the transformation parameters, as well as an alternative Procrustes method using Singular Value Decomposition (SVD). These two approaches underscore the flexibility of 3DRegNet when it comes to handling varied registration challenges.
3. Refinement Network: A supplementary smaller 3DRegNet augmentation network serves as a refinement module, enhancing registration accuracy further. This additional network attempts to fine-tune the initial rough registration for more exact results.

Experimental Results and Comparisons

3DRegNet demonstrates its prowess through rigorous experimentation on datasets like ICL-NUIM and SUN3D. Results indicate that 3DRegNet outperforms traditional methods such as Fast Global Registration (FGR) and Iterative Closest Point (ICP). Specifically, the network showcases superior performance for more complex registration problems, as evidenced by superior rotation and translation accuracy when compared to competing methods.

For instance, in comparing 3DRegNet to FGR using cumulative distribution functions (CDF) on the SUN3D dataset, it is revealed that 3DRegNet excels where the complexity and noise in the registration challenge increase. The paper highlights that while traditional techniques perform adequately for minor transformations, 3DRegNet offers marked improvements as the problem complexity escalates.

Implications and Future Directions

The introduction of 3DRegNet marks a significant step forward in leveraging neural networks for geometric problems like 3D point registration. The paper suggests potential applications and improvements over established methods, due to its efficiency and adaptability in solving complex registration tasks.

From a practical standpoint, 3DRegNet's deployment can significantly reduce computational overheads, a consideration critical in real-time applications such as autonomous navigation and augmented reality. Theoretical implications point towards deep learning's capability to redefine traditional approaches in spatial transformations by integrating more robust and generalized learning mechanisms.

As for future developments, further exploration into optimizing the network's architecture, expanding its applicability to a wider range of scenes and datasets, and integrating newer deep learning advancements could enhance the model's performance further. Additionally, potential extensions into multi-Degree of Freedom (DoF) pose estimation and handling diverse sensor data are avenues ripe for exploration, building on 3DRegNet's foundational work.

Overall, this paper presents a significant contribution to 3D registration through its innovative use of deep learning, positioning 3DRegNet as an essential tool for researchers and practitioners in computer vision and beyond.