TILDE: A Temporally Invariant Learned DEtector

Abstract: We introduce a learning-based approach to detect repeatable keypoints under drastic imaging changes of weather and lighting conditions to which state-of-the-art keypoint detectors are surprisingly sensitive. We first identify good keypoint candidates in multiple training images taken from the same viewpoint. We then train a regressor to predict a score map whose maxima are those points so that they can be found by simple non-maximum suppression. As there are no standard datasets to test the influence of these kinds of changes, we created our own, which we will make publicly available. We will show that our method significantly outperforms the state-of-the-art methods in such challenging conditions, while still achieving state-of-the-art performance on the untrained standard Oxford dataset.

TILDE: A Temporally Invariant Learned Detector

The paper introduces TILDE, a method for learning-based keypoint detection which is robust to drastic changes in weather and lighting conditions—a challenge where many traditional detectors falter. The core of the proposed technique lies in a robust regression model, trained to predict keypoint score maps that remain consistent across varying conditions, thus allowing for successful non-maximum suppression to identify stable points of interest.

Summary of Contributions

1. Detector Architecture: TILDE leverages a piece-wise linear regression model based on Generalized Hinging Hyperplanes (GHH). This design combines efficiency with sufficient complexity to accommodate the nonlinear appearance variations inherent in outdoor scenes. The regression model produces a score map, with local maxima indicating keypoints.

2. Training Methodology: The lack of available datasets for testing these variations of imaging conditions prompted the authors to create a custom dataset from the Archive of Many Outdoor Scenes (AMOS) and their panoramic setups. Positive training samples are derived from images taken across varying illuminations but consistent viewpoints, enhancing the learning capacity to identify keypoint location invariance and robustness.

3. Evaluation and Results: The method's efficacy is substantiated through comparisons to existing state-of-the-art keypoint detectors such as SIFT, SURF, and FAST, among others. The tests were conducted in challenging conditions using both their custom dataset and standard datasets like Oxford and EF. The TILDE model exhibits superior repeatability of keypoints without loss of performance in more conventional datasets, affirming its versatility and robustness.

Key Numerical Results

• On the custom Webcam dataset, TILDE achieves a notable 48.3% in repeatability under extreme conditions, outperforming the best competing method, SURF, by a significant margin.
• When deployed on traditional datasets such as Oxford, TILDE maintains competitive performance, specifically remarkable in outdoor images affected by illumination shift, reinforcing its adaptability.

Insights and Implications

The implications of TILDE's success are considerable for computer vision applications requiring consistency under variable environmental conditions, like autonomous driving and outdoor scene reconstruction. The ability to reliably detect keypoints, regardless of temporal variances, ensures more robust foundational data for subsequent tasks such as image registration and tracking.

The theoretical underpinning of using piece-wise linear regressors coupled with innovative training regimes introduced by TILDE opens potential future explorations in scale invariant detection and applications in dynamic scene understanding. Adaptation to scale transformations and more sophisticated integration with neural networks could further enhance its applicability and performance.

Future Developments

Given the foundational nature of keypoint detection across various computer vision tasks, the refinement and exploration of the TILDE methodology could significantly impact the efficiency and reliability of numerous applications. Improvement through the scale-space extension and integration with other domains such as SLAM and AR appears promising. Further focus on computational efficiency and real-time processing capabilities would support broader application in resource-constrained environments.

This paper's contribution represents a step forward in dealing with the intricacies of keypoint detection under challenging temporal variations, laying the groundwork for continued enhancements in robust feature detection.