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Paper Detail

Paper IDIMT-CIF-1.11
Paper Title LIGHTS: LIGHT SPECULARITY DATASET FOR SPECULAR DETECTION IN MULTI-VIEW
Authors Mohamed Dahy Elkhouly, Istituto Italiano di Tecnologia, Universit`a degli studi di Genova, Italy; Theodore Tsesmelis, Alessio Del Bue, Stuart James, Istituto Italiano di Tecnologia, Italy
SessionIMT-CIF-1: Computational Imaging 1
LocationArea J
Session Time:Monday, 20 September, 13:30 - 15:00
Presentation Time:Monday, 20 September, 13:30 - 15:00
Presentation Poster
Topic Computational Image Formation: Multi-image & sensor fusion
IEEE Xplore Open Preview  Click here to view in IEEE Xplore
Abstract Specular highlights are commonplace in images, however, methods for detecting them and removing the phenomenon are particularly challenging. A reason for this is the difficulty in creating a dataset for training or evaluation, as in the real world, we lack the necessary control over the environment. Therefore, we propose a novel physically-based rendered LIGHT Specularity (LIGHTS) Dataset for the evaluation of the specular highlight detection task. Our dataset consists of 18 high-quality architectural scenes, where each scene is rendered with multiple views. In total, the dataset contains 2,603 views with an average of 145 views per scene. Additionally, we propose a simple aggregation based method for specular highlight detection that outperforms prior work by 3.6% in two orders of magnitude less time on our dataset.