ReF -- Rotation Equivariant Features for Local Feature Matching

• URL: http://arxiv.org/abs/2203.05206v1
• Date: Thu, 10 Mar 2022 07:36:09 GMT
• Title: ReF -- Rotation Equivariant Features for Local Feature Matching
• Authors: Abhishek Peri, Kinal Mehta, Avneesh Mishra, Michael Milford, Sourav Garg, K. Madhava Krishna
• Abstract summary: We propose an alternative, complementary approach that centers on inducing bias in the model architecture itself to generate rotation-specific' features. We demonstrate that this high performance, rotation-specific coverage from the steerable CNNs can be expanded to all rotation angles. We present a detailed analysis of the performance effects of ensembling, robust estimation, network architecture variations, and the use of rotation priors.
• Score: 30.459559206664427
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Sparse local feature matching is pivotal for many computer vision and robotics tasks. To improve their invariance to challenging appearance conditions and viewing angles, and hence their usefulness, existing learning-based methods have primarily focused on data augmentation-based training. In this work, we propose an alternative, complementary approach that centers on inducing bias in the model architecture itself to generate `rotation-specific' features using Steerable E2-CNNs, that are then group-pooled to achieve rotation-invariant local features. We demonstrate that this high performance, rotation-specific coverage from the steerable CNNs can be expanded to all rotation angles by combining it with augmentation-trained standard CNNs which have broader coverage but are often inaccurate, thus creating a state-of-the-art rotation-robust local feature matcher. We benchmark our proposed methods against existing techniques on HPatches and a newly proposed UrbanScenes3D-Air dataset for visual place recognition. Furthermore, we present a detailed analysis of the performance effects of ensembling, robust estimation, network architecture variations, and the use of rotation priors.

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