Residual Learning for Image Point Descriptors
(2312.15471)Abstract
Local image feature descriptors have had a tremendous impact on the development and application of computer vision methods. It is therefore unsurprising that significant efforts are being made for learning-based image point descriptors. However, the advantage of learned methods over handcrafted methods in real applications is subtle and more nuanced than expected. Moreover, handcrafted descriptors such as SIFT and SURF still perform better point localization in Structure-from-Motion (SfM) compared to many learned counterparts. In this paper, we propose a very simple and effective approach to learning local image descriptors by using a hand-crafted detector and descriptor. Specifically, we choose to learn only the descriptors, supported by handcrafted descriptors while discarding the point localization head. We optimize the final descriptor by leveraging the knowledge already present in the handcrafted descriptor. Such an approach of optimization allows us to discard learning knowledge already present in non-differentiable functions such as the hand-crafted descriptors and only learn the residual knowledge in the main network branch. This offers 50X convergence speed compared to the standard baseline architecture of SuperPoint while at inference the combined descriptor provides superior performance over the learned and hand-crafted descriptors. This is done with minor increase in the computations over the baseline learned descriptor. Our approach has potential applications in ensemble learning and learning with non-differentiable functions. We perform experiments in matching, camera localization and Structure-from-Motion in order to showcase the advantages of our approach.
Overview
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This paper proposes a hybrid method for local image feature description, combining handcrafted descriptors with deep learning to improve precision in point localization.
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Challenges in self-supervised learning for descriptors are addressed, with a focus on sub-pixel localization impacting 3D reconstructions in SfM.
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The method employs a two-step process using handcrafted methods like SIFT or SURF for keypoint detection followed by refinement through deep neural networks.
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Self-supervised training is used on the MS COCO dataset, and the hybrid method shows improvement over both handcrafted descriptors and the SuperPoint baseline in experiments.
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The study illustrates that a blend of machine learning and traditional techniques can lead to efficient algorithms with enhanced capabilities in computer vision applications.
Introduction
Local image feature descriptors are crucial in computer vision, with applications ranging from Structure-from-Motion (SfM) to Simultaneous Localization and Mapping (SLAM). They are generally classified into handcrafted descriptors, like SIFT and SURF, and learned descriptors obtained through methods such as deep learning. While learned descriptors benefit from advancements in self-supervised learning and neural networks, they often fall short in precise point localization compared to handcrafted ones. In response, this paper introduces a hybrid method that combines the strength of handcrafted descriptors in point localization with the power of deep learning to learn the residual knowledge beyond what's captured by handcrafted methods.
Related Work and Challenges
Prevailing approaches for local image point description are to fully learn both keypoints and descriptors or improve upon handcrafted methods. Self-supervised learning offers an avenue to train descriptors by using image augmentations, but it presents challenges in sub-pixel point localization. This issue of low ‘resolution’ in point clouds impacts the quality of 3D reconstruction in SfM. Existing methods like the SuperPoint network mitigate these problems to some extent but require high computational resources, rendering them less suitable for real-time or resource-constrained applications.
Methodology
The proposed method focuses on learning a descriptor using deep neural networks (DNNs) conditioned on keypoints detected by a handcrafted method. This technique involves a two-step process where first, a handcrafted method like SIFT or SURF is applied to detect keypoints. Then, a DNN refines these keypoints by learning additional characteristics not captured by the initial method. This fusion of handcrafted precision with DNN's ability to learn nuanced patterns enables more accurate and reliable descriptors that are still computationally efficient. The combined descriptors are optimized through self-supervised training on the MS COCO dataset with metric learning, and extensive evaluations are performed on various benchmarks.
Experiments and Conclusions
The hybrid method outperforms handcrafted methods and the SuperPoint baseline across numerous metrics on matching and camera localization tasks. Its key advantage is learning only what’s missing in the handcrafted descriptors, enabling it to add meaningful information. This distinction allows for faster convergence during training compared to fully learned counterparts. While the method introduces an overhead due to the DNN, it offers a balanced tradeoff between computational efficiency and performance for image descriptors. In closing, the study demonstrates how machine learning can complement traditional techniques to push the boundaries of what's possible in computer vision, paving a way for more sophisticated yet efficient algorithms.
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