A 4D Light-Field Dataset and CNN Architectures for Material Recognition
This work addresses material recognition for computer vision researchers by providing a first mid-size light-field dataset and baseline methods, though it is incremental in applying CNNs to a new data type.
The authors tackled material recognition by introducing a new 4D light-field dataset and novel CNN architectures, achieving a 7% accuracy boost from 70% to 77% compared to 2D image classification.
We introduce a new light-field dataset of materials, and take advantage of the recent success of deep learning to perform material recognition on the 4D light-field. Our dataset contains 12 material categories, each with 100 images taken with a Lytro Illum, from which we extract about 30,000 patches in total. To the best of our knowledge, this is the first mid-size dataset for light-field images. Our main goal is to investigate whether the additional information in a light-field (such as multiple sub-aperture views and view-dependent reflectance effects) can aid material recognition. Since recognition networks have not been trained on 4D images before, we propose and compare several novel CNN architectures to train on light-field images. In our experiments, the best performing CNN architecture achieves a 7% boost compared with 2D image classification (70% to 77%). These results constitute important baselines that can spur further research in the use of CNNs for light-field applications. Upon publication, our dataset also enables other novel applications of light-fields, including object detection, image segmentation and view interpolation.