USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds (1904.00229v1)

Abstract: In this paper, we propose the USIP detector: an Unsupervised Stable Interest Point detector that can detect highly repeatable and accurately localized keypoints from 3D point clouds under arbitrary transformations without the need for any ground truth training data. Our USIP detector consists of a feature proposal network that learns stable keypoints from input 3D point clouds and their respective transformed pairs from randomly generated transformations. We provide degeneracy analysis of our USIP detector and suggest solutions to prevent it. We encourage high repeatability and accurate localization of the keypoints with a probabilistic chamfer loss that minimizes the distances between the detected keypoints from the training point cloud pairs. Extensive experimental results of repeatability tests on several simulated and real-world 3D point cloud datasets from Lidar, RGB-D and CAD models show that our USIP detector significantly outperforms existing hand-crafted and deep learning-based 3D keypoint detectors. Our code is available at the project website. https://github.com/lijx10/USIP

• The paper introduces an unsupervised detector that learns transformation-invariant 3D keypoints using randomly transformed point cloud pairs.
• It leverages a deep Feature Proposal Network with probabilistic chamfer loss to ensure precise localization and mitigate degeneracy issues.
• Empirical results across various datasets show USIP outperforming both handcrafted and other deep learning methods in repeatability, robustness, and efficiency.

Unsupervised Stable Interest Point detection from 3D Point Clouds

The paper "USIP: Unsupervised Stable Interest Point Detection from 3D Point Clouds" presents an innovative approach to 3D keypoint detection in point clouds without relying on ground truth data for training. The authors introduce the USIP (Unsupervised Stable Interest Point) detector, which harnesses a deep learning-based Feature Proposal Network (FPN) to identify highly repeatable and precisely localized keypoints in 3D data, even under arbitrary transformations. This landmark work highlights its superiority over traditional hand-crafted detectors and existing deep learning-based methods, providing significant contributions to both theoretical understanding and practical applications.

The core of the USIP detector is its unsupervised learning methodology, which circumvents the necessity for labeled data typically required by supervised learning techniques. Instead, the detector is trained using randomly transformed pairs of point clouds, enabling it to learn robust keypoints by minimizing distances between its outputs via a probabilistic chamfer loss. This approach ensures keypoints remain consistent across varying perspectives and perturbations, addressing a critical challenge in 3D data processing.

Contributions and Methodological Advances

1. Unsupervised Framework: The USIP detector is particularly noteworthy for its unsupervised nature, avoiding ground truth requirements that are practically challenging to obtain. The reliance on arbitrary transformation pairs during training is a significant advancement, facilitating the learning of transformation-invariant keypoints.
2. Degeneracy Prevention: Through analytical explorations, the authors identify potential degeneracies in the network, such as the trivial solutions where the network might simply output the point cloud's centroid or align points along principal axes. The paper provides solutions to these issues, maintaining the network's robustness and effectiveness.
3. Feature Proposal Network: The FPN, at the heart of USIP, proposes keypoints by estimating their optimal positions, thereby reducing quantization errors common in selecting keypoints from existing cloud points. This network's design, coupled with the point-to-point loss that confines keypoints close to the original point cloud, sets a new standard in keypoint localization accuracy.
4. Empirical Evaluation: The paper presents rigorously conducted experiments across datasets from object models, outdoor Lidar, and indoor RGB-D scans. In repeatability tests, the USIP detector significantly outperforms both handcrafted algorithms and existing deep learning-based detectors, such as 3DFeat-Net. The results illustrate that the detector is more resilient to variations in noise, density, and coverage—a testament to its robustness and broad applicability.
5. Computational Efficiency: In addition to its performance, USIP shows remarkable computational efficiency, being orders of magnitude faster than many existing methods, highlighting its potential for real-time applications.

Implications and Future Work

The implications of the USIP detector are profound, particularly in fields such as robotics, autonomous driving, and 3D modeling where stable feature detection is pivotal. By eliminating the need for labeled data, the proposed approach drastically reduces the barriers to deploying advanced 3D computer vision applications at scale.

Theoretically, this unsupervised learning framework paves the way for further exploration into learning transferable, invariant features across domains and scenarios without direct supervision. Practically, the significant improvements in repeatability and localization accuracy promise substantial enhancements in subsequent tasks like registration and recognition.

For future developments, extending the unsupervised framework of USIP to integrate more complex scene understanding tasks, such as semantic segmentation or scene reconstruction, could be valuable. Moreover, exploring the integration of temporal dynamics with this framework may unlock new potentials for 4D data processing in dynamic environments.

In conclusion, the USIP detector represents a significant stride in advancing 3D point cloud processing. Its novel unsupervised learning paradigm, coupled with robust experimental validation, underscores its potential to redefine keypoint detection standards in computer vision and robotics.