SiLK -- Simple Learned Keypoints (2304.06194v1)

Abstract: Keypoint detection & descriptors are foundational tech-nologies for computer vision tasks like image matching, 3D reconstruction and visual odometry. Hand-engineered methods like Harris corners, SIFT, and HOG descriptors have been used for decades; more recently, there has been a trend to introduce learning in an attempt to improve keypoint detectors. On inspection however, the results are difficult to interpret; recent learning-based methods employ a vast diversity of experimental setups and design choices: empirical results are often reported using different backbones, protocols, datasets, types of supervisions or tasks. Since these differences are often coupled together, it raises a natural question on what makes a good learned keypoint detector. In this work, we revisit the design of existing keypoint detectors by deconstructing their methodologies and identifying the key components. We re-design each component from first-principle and propose Simple Learned Keypoints (SiLK) that is fully-differentiable, lightweight, and flexible. Despite its simplicity, SiLK advances new state-of-the-art on Detection Repeatability and Homography Estimation tasks on HPatches and 3D Point-Cloud Registration task on ScanNet, and achieves competitive performance to state-of-the-art on camera pose estimation in 2022 Image Matching Challenge and ScanNet.

• The paper presents a simplified architecture that foregoes traditional complexities like NMS, using learned probabilities for keypoint detection.
• The paper employs a self-supervised, cycle-consistent framework that trains on arbitrary image data to ensure distinctive and robust keypoints.
• The paper demonstrates high performance on benchmarks such as HPatches and ScanNet, highlighting its potential for efficient real-time applications.

Overview of "SiLK : Simple Learned Keypoints"

The paper "SiLK: Simple Learned Keypoints" introduces a novel approach to keypoint detection in computer vision, challenging the necessity of complex frameworks by proposing a simpler yet effective model. SiLK capitalizes on a fully differentiable, lightweight architecture to achieve state-of-the-art performance on several keypoint-related tasks without relying on traditional complexities like context aggregation or extensive data supervision.

Key Contributions

1. Simplified Architecture: SiLK's architecture is characterized by its minimalistic backbone, facilitating straightforward training and deployment. Unlike traditional models, SiLK does not employ non-maximum suppression (NMS) during inference, opting instead for a detection mechanism based on learned probabilities of matching success.
2. Self-Supervised Learning: The model uses self-supervision to train on arbitrary image data, eliminating the need for annotated datasets. This is achieved by leveraging a cycle-consistent probabilistic framework that encourages keypoints to be distinctive and resistant to transformations in viewpoint or lighting.
3. High Performance: Empirical evaluations demonstrate SiLK's superior performance on challenging benchmarks like HPatches, the IMC 2022 outdoor pose estimation challenge, and ScanNet indoor tasks. The model surpasses existing solutions by achieving higher repeatability and accuracy in homography estimation tasks.
4. Robustness and Flexibility: Through extensive ablation studies, SiLK displays robust performance across diverse datasets, backbones, and resolutions. This adaptability suggests its potential application in various real-time scenarios, where computational efficiency is paramount.

Technical Insights

• Descriptor Learning: SiLK employs a probabilistic double-softmax methodology for descriptor learning, maximizing the likelihood of correct round-trip matches to ensure geometric consistency.
• Lightweight Backbone: The backbone architecture, derived from VGG, is stripped of max-pooling and up-sampling layers, emphasizing computational simplicity without sacrificing performance.
• Algorithmic Simplicity: The method streamlines keypoint detection to a single-stage training process, enhancing repeatability metrics without introducing the overhead associated with context-aware approaches like transformers or graph neural networks.

Empirical Results

SiLK achieves top results in repeatability and homography estimation on HPatches, indicating its capability for pixel-level precision. It excels in scenarios with varying image resolutions and exhibits strong generalization across diverse datasets like COCO, ImageNet, MegaDepth, and ScanNet. The model's minimalistic nature allows it to perform efficiently in competitive benchmarks while sustaining low computational costs.

Theoretical and Practical Implications

Practically, SiLK provides a compelling alternative for applications where real-time and on-device processing constraints exist, such as augmented reality and autonomous navigation. Theoretically, the model's success questions whether current paradigms necessitate the complexity they typically involve, opening avenues for further exploration into efficient design for learned keypoints.

Future Directions

The potential for incorporating contextual information into SiLK's design, without undue complexity, remains an open area for research. Additionally, expanded evaluation on a wider assortment of computer vision tasks could further cement its utility in the field.

In summary, SiLK represents a significant step forward in simplifying learned keypoint detection without compromising performance, thus contributing to both the theoretical understanding and practical utility within computer vision.