Paper ID | IMT-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 | ||
Session | IMT-CIF-1: Computational Imaging 1 | ||
Location | Area 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. |