SuperPoint: Self-Supervised Interest Point Detection and Description (1712.07629v4)

Abstract: This paper presents a self-supervised framework for training interest point detectors and descriptors suitable for a large number of multiple-view geometry problems in computer vision. As opposed to patch-based neural networks, our fully-convolutional model operates on full-sized images and jointly computes pixel-level interest point locations and associated descriptors in one forward pass. We introduce Homographic Adaptation, a multi-scale, multi-homography approach for boosting interest point detection repeatability and performing cross-domain adaptation (e.g., synthetic-to-real). Our model, when trained on the MS-COCO generic image dataset using Homographic Adaptation, is able to repeatedly detect a much richer set of interest points than the initial pre-adapted deep model and any other traditional corner detector. The final system gives rise to state-of-the-art homography estimation results on HPatches when compared to LIFT, SIFT and ORB.

• The paper introduces SuperPoint, a framework that integrates interest point detection and descriptor computation in a single, efficient, self-supervised network.
• Its methodology leverages Homographic Adaptation to enhance robustness and repeatability under various image transformations.
• Experimental results show high repeatability and superior descriptor quality, making it effective for real-time applications like SLAM and AR.

SuperPoint: Self-Supervised Interest Point Detection and Description

The paper "SuperPoint: Self-Supervised Interest Point Detection and Description" introduces an advanced framework for interest point detection and description in the context of multiple-view geometry problems in computer vision. This framework, named SuperPoint, diverges from conventional patch-based neural networks by leveraging a fully-convolutional model capable of processing full-sized images in one forward pass to jointly compute pixel-level interest point locations and their descriptors. The SuperPoint model is also complemented by a novel training regime called Homographic Adaptation, which enhances the repeatability of interest points through multi-scale and multi-homography adaptations, achieving superior cross-domain performance.

Model and Training Regime

The SuperPoint system is structured as a fully-convolutional neural network that operates over entire images, different from traditional systems that may use patches or sequential processing. A critical innovation of this system is the Homographic Adaptation process, which augments the training of the network by applying random homographies to the input images, thereby improving the detector’s robustness and repeatability under various transformations.

The architecture comprises a shared encoder based on a VGG-like design that reduces the image’s spatial dimensions while increasing the feature depth. Following this encoder, two decoder heads specialize in interest point detection and descriptor computation, respectively. The interest point decoder employs a sub-pixel convolution strategy to output a dense map of interest points, while the descriptor decoder uses a semi-dense strategy, later upsampled to the full resolution. The training procedure includes a self-supervised approach, initially training on synthetic shapes and subsequently leveraging Homographic Adaptation to fine-tune on real-world images from datasets such as MS-COCO.

Experimental Evaluation

The efficacy of SuperPoint is demonstrated through rigorous quantitative evaluations on multiple benchmarks. Key results include:

1. Interest Point Detection Repeatability on HPatches:
   • SuperPoint shows superior performance under illumination changes and competitive performance under viewpoint changes. Specifically, it achieves a repeatability score of 0.652 on illumination scenes and 0.503 on viewpoint scenes with Non-Maximum Suppression (NMS) set to 4.
2. Homography Estimation:
   • When evaluated on the HPatches dataset, the SuperPoint model outperforms LIFT and ORB and performs comparably to SIFT in homography estimation, particularly using thresholds of ε=3 and ε=5. SuperPoint achieves an accuracy of 0.684 and 0.829 for these thresholds, respectively.
3. Performance Metrics:
   • The SuperPoint model achieves the highest scores in descriptor-related metrics such as nearest neighbor mean Average Precision (NN mAP) and matching score (M. Score), indicating robust descriptor quality for high-level semantic tasks.

Theoretical and Practical Implications

The development and results of SuperPoint provide several theoretical and practical implications for the field of computer vision:

• Theoretical Implications:
  • The integration of detection and description into a single end-to-end trainable pipeline offers a conceptual simplification while preserving computational efficiency.
  • The self-supervised nature of the training through Homographic Adaptation demonstrates the potential for models to improve without extensive labeled datasets, important for domains with limited annotative resources.
• Practical Implications:
  • Enhanced repeatability and descriptor quality directly impact tasks such as Simultaneous Localization and Mapping (SLAM), Structure-from-Motion (SfM), and image matching, making SuperPoint highly suitable for real-time applications in robotics and augmented reality.
  • The efficient run-time of approximately 70 FPS on high-resolution images underscores the potential for deployment in real-time systems.

Future Developments in AI

The SuperPoint framework marks a significant step forward in robust feature detection and description, suggesting multiple avenues for future research:

1. Expanded Self-Supervised Techniques:
   • Future models may build upon the principles of Homographic Adaptation, exploring other forms of self-supervision and adaptation across varied domains and applications.
2. Generative Models:
   • Investigating the integration with generative models to enhance interest point diversity and robustness across more complex transformations could further advance model performance.
3. Broader Applications:
   • Extending the framework to other dense prediction tasks, such as semantic segmentation and object detection, may yield promising results, leveraging similar self-supervised adaptation techniques.

In conclusion, the SuperPoint paper makes a significant contribution to the field of interest point detection and description, offering a sophisticated, efficient, and self-supervised solution with demonstrable performance improvements across key computer vision benchmarks. The implications of this work extend broadly, suggesting further innovations and applications within the domain of AI and computer vision.