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About ALCN
ALCN is a novel illumination normalization method. It lets us learn to detect objects and estimate their 3D poses under challenging illumination conditions from very few training samples. This work was supported by the Christian Doppler Laboratory for Semantic 3D Computer Vision, funded in part by Qualcomm Inc.
ALCN Dataset
The ALCN dataset consists of ALCN-2D and ALCN-Duck datasets:
ALCN-2D for benchmarking object detection under challenging light conditions and cluttered background. We select three objects spanning different material properties: plastic, velvet and metal (velvet has a BRDF that is neither Lambertian nor specular, and the metallic object -- the watch -- is very specular). For each object, we have 10 grayscale 300x300 real trainng images and 1200 1280x800 grayscale test images, exhibiting these objects under different illuminations, different lighting colors, and distractors in the background.