MaskNet: A Fully-Convolutional Network to Estimate Inlier Points (2010.09185v1)

Abstract: Point clouds have grown in importance in the way computers perceive the world. From LIDAR sensors in autonomous cars and drones to the time of flight and stereo vision systems in our phones, point clouds are everywhere. Despite their ubiquity, point clouds in the real world are often missing points because of sensor limitations or occlusions, or contain extraneous points from sensor noise or artifacts. These problems challenge algorithms that require computing correspondences between a pair of point clouds. Therefore, this paper presents a fully-convolutional neural network that identifies which points in one point cloud are most similar (inliers) to the points in another. We show improvements in learning-based and classical point cloud registration approaches when retrofitted with our network. We demonstrate these improvements on synthetic and real-world datasets. Finally, our network produces impressive results on test datasets that were unseen during training, thus exhibiting generalizability. Code and videos are available at https://github.com/vinits5/masknet

• The paper introduces MaskNet, a fully-convolutional neural network that estimates inlier points in 3D point clouds using an MLP-based convolutional architecture.
• MaskNet achieves superior computational efficiency and outperforms state-of-the-art methods like PRNet and RPM-Net in identifying inliers on both synthetic and real-world datasets.
• This network improves downstream tasks by serving as an effective preprocessing step, significantly enhancing accuracy in point cloud registration and denoising when used with existing algorithms.

Overview of MaskNet: A Fully-Convolutional Network to Estimate Inlier Points in Point Clouds

The paper presents "MaskNet," a fully-convolutional neural network designed to tackle the challenging task of estimating inlier points within point clouds, which are crucial for enhanced data processing applications like registration, object detection, and tracking. Point clouds play a vital role in computer vision, particularly with the proliferation of LIDAR sensors and other 3D vision technologies. However, point clouds often suffer from imperfections such as missing or extraneous points, primarily due to sensor limitations or environmental occlusions. These discrepancies significantly impede algorithms that compute correspondences between point clouds. MaskNet addresses this ordeal by leveraging a deep learning approach to enhance the accuracy and efficiency of inlier point estimation, thereby improving point cloud processing capabilities.

Key Contributions

• Fully-Convolutional Network Architecture: The paper introduces a model that employs multi-layer perceptrons (MLPs) in a convolutional architecture to process and learn from point cloud data, facilitating the extraction of invariant features crucial for estimating inliers.
• Iterative Improvement Approach: MaskNet is demonstrated to iteratively refine its estimation of inlier points, showing improvements in precision with each iteration. This refinement is critical, considering the initial misalignments typically observed in practical applications.
• Superior Computational Efficiency: Compared to traditional methods like RANSAC and optimization-based techniques, MaskNet's learning-based framework yields faster and more computationally efficient results, making it suitable for real-time applications.

Empirical Evaluation

The efficacy of MaskNet was validated on synthetic datasets from ModelNet40 and real-world datasets such as S3DIS and 3DMatch. The experiments underline the capability of MaskNet to outperform state-of-the-art approaches like PRNet and RPM-Net in identifying inliers. Specifically, MaskNet demonstrated strong performance even with varying percentages of missing points in the partial point clouds.

Moreover, MaskNet's effectiveness extends to denoising tasks, where it efficiently identifies and removes outlier points when compared against a reference template of the same category. This property is particularly beneficial for applications requiring point cloud refinement before conducting further analyses.

The ability of MaskNet to work seamlessly with existing registration algorithms (e.g., ICP and Deep Closest Point) as a preprocessing add-on was also highlighted, showcasing significant improvements in both rotation and translation error metrics during point cloud registration tasks.

Implications and Future Directions

The development of MaskNet offers meaningful implications for enhancing point cloud-based applications across multiple domains, from autonomous navigation to computer-assisted design. The network's design and demonstrated generalizability across both seen and unseen object categories advocate for its adoption in diverse practical scenarios.

Future research directions could explore the adaptation of MaskNet to unsupervised or semi-supervised learning paradigms, potentially increasing its robustness and applicability without extensive labeled data. Additionally, replacing the PointNet encoding with more sophisticated representations that are less sensitive to noise and invariant to pose transformations could further bolter the network's effectiveness.

In summary, MaskNet represents a significant advancement in point cloud analysis, providing an efficient, scalable, and versatile solution to the challenge of inlier estimation. Its incorporation into existing point cloud processing pipelines stands to considerably elevate the precision and reliability of downstream tasks.