# Robust Angular Local Descriptor Learning

## TL;DR

This paper introduces a robust angular loss for local descriptor learning that improves robustness and generalization by using cosine similarity and a loss function less sensitive to incorrect correspondences, achieving state-of-the-art results.

## Contribution

The paper proposes a novel angular loss function that enhances local descriptor robustness and generalization, addressing issues with previous margin-based losses.

## Key findings

- Achieves state-of-the-art performance on Brown dataset
- Demonstrates strong generalization on Hpatches and Wide Baseline Stereo datasets
- Improves robustness against incorrect correspondences

## Abstract

In recent years, the learned local descriptors have outperformed handcrafted ones by a large margin, due to the powerful deep convolutional neural network architectures such as L2-Net [1] and triplet based metric learning [2]. However, there are two problems in the current methods, which hinders the overall performance. Firstly, the widely-used margin loss is sensitive to incorrect correspondences, which are prevalent in the existing local descriptor learning datasets. Second, the L2 distance ignores the fact that the feature vectors have been normalized to unit norm. To tackle these two problems and further boost the performance, we propose a robust angular loss which 1) uses cosine similarity instead of L2 distance to compare descriptors and 2) relies on a robust loss function that gives smaller penalty to triplets with negative relative similarity. The resulting descriptor shows robustness on different datasets, reaching the state-of-the-art result on Brown dataset , as well as demonstrating excellent generalization ability on the Hpatches dataset and a Wide Baseline Stereo dataset.