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PCRNet: Point Cloud Registration Network using PointNet Encoding (1908.07906v2)

Published 21 Aug 2019 in cs.CV

Abstract: PointNet has recently emerged as a popular representation for unstructured point cloud data, allowing application of deep learning to tasks such as object detection, segmentation and shape completion. However, recent works in literature have shown the sensitivity of the PointNet representation to pose misalignment. This paper presents a novel framework that uses the PointNet representation to align point clouds and perform registration for applications such as tracking, 3D reconstruction and pose estimation. We develop a framework that compares PointNet features of template and source point clouds to find the transformation that aligns them accurately. Depending on the prior information about the shape of the object formed by the point clouds, our framework can produce approaches that are shape specific or general to unseen shapes. The shape specific approach uses a Siamese architecture with fully connected (FC) layers and is robust to noise and initial misalignment in data. We perform extensive simulation and real-world experiments to validate the efficacy of our approach and compare the performance with state-of-art approaches.

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Authors (7)
  1. Vinit Sarode (5 papers)
  2. Xueqian Li (20 papers)
  3. Hunter Goforth (5 papers)
  4. Yasuhiro Aoki (3 papers)
  5. Rangaprasad Arun Srivatsan (11 papers)
  6. Simon Lucey (107 papers)
  7. Howie Choset (92 papers)
Citations (214)

Summary

  • The paper introduces PCRNet, a deep learning framework using a Siamese network with PointNet encoding to reliably compute transformations for point cloud registration.
  • It combines iterative refinement with fully connected layers to boost robustness against noise and misalignment, outperforming traditional methods like ICP and Go-ICP.
  • PCRNet achieves high computational efficiency and precision in both general and object-specific scenarios, offering practical value for autonomous driving and robotic applications.

An Examination of PCRNet for Point Cloud Registration

The paper introduces PCRNet, a novel framework for point cloud registration using the PointNet architecture as an encoding mechanism. Point cloud registration is a key task in various applications such as autonomous driving, 3D reconstruction, and robotic manipulation, which require aligning different sets of point cloud data accurately. PointNet has shown significant promise in handling unstructured point cloud data, but it remains sensitive to misalignments in pose, which PCRNet aims to address explicitly.

PCRNet operates by comparing features derived from both template and source point clouds to determine the transformation that minimizes their misalignment. The framework supports both shape-specific and general approaches for alignment, depending on the available prior information regarding object shapes. It utilizes a Siamese architecture combined with fully connected layers, which enhances robustness to noise and initial misalignment. The paper claims substantial improvements over traditional registration techniques like ICP (Iterative Closest Point) and other machine learning-based methods, particularly in handling noisy data and achieving computational efficiency.

Approach

PCRNet leverages a Siamese network architecture where PointNet is used to encode template and source point clouds into feature vectors. The framework then employs fully connected layers to predict transformations aligning these features. For increasing robustness, an iterative version of PCRNet is developed, using an iterative refinement approach akin to traditional algorithms but integrating seamlessly with deep learning architectures.

The training of PCRNet is conducted on the ModelNet40 dataset, involving both multi-category and single-object scenarios to test generalization versus specificity. The PointNetLK method serves as a baseline for comparisons, alongside other traditional approaches like ICP and Go-ICP.

Numerical Results and Claims

The paper demonstrates that PCRNet offers improved registration accuracy and speed in several scenarios:

  • In the presence of Gaussian noise, iterative PCRNet outperforms classical algorithms such as ICP and computationally intensive methods such as Go-ICP in both accuracy and processing time, with iterative PCRNet achieving accuracy comparable to globally optimal solutions while being significantly faster.
  • By leveraging prior object-specific information, PCRNet displays superior precision over generalizable models when applied within known object categories, emphasizing the benefit of incorporating data-driven priors.

Implications and Future Directions

PCRNet represents a significant development in the field of point cloud processing. By providing an adaptable framework that can incorporate object-specific information, it sets the stage for more responsive and context-aware registration techniques. This is particularly relevant for dynamic environments such as autonomous vehicles and robotic systems, where real-time processing of sensory data is critical.

From a theoretical perspective, PCRNet sheds light on the benefits and limitations of deep learning models in rigid body transformation tasks, suggesting pathways for further refinement of feature extraction and transformation prediction networks.

In practice, the ability to integrate iterative refinement within an end-to-end learning framework could facilitate its adoption in systems requiring quick and accurate registration. The authors' open sharing of code contributes positively towards this goal, inviting further exploration and optimization by the research community.

Future research may focus on extending PCRNet’s capabilities to handle partial or occluded point clouds, improving its adaptability to incomplete data which is common in real-world scenarios. Moreover, exploring synergy with other deep learning architectures could provide further gains in robustness and performance, potentially expanding its applicability to a broader range of 3D vision tasks.

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