It is commonly believed that image recognition based on RGB improves when using RGB-D, ie: when depth information (distance from the camera) is added. Adding depth should make models more robust to appearance variations in colors and lighting; to recognize shape and spatial relationships while allowing models to ignore irrelevant backgrounds. In this paper we investigate how robust current RGB-D models truly are to changes in appearance, depth, and background where we vary one modality (RGB or depth) and compare RGB-D to RGB-only and depth-only in a semantic segmentation setting. Experiments show that all investigated RGB-D models show some robustness to variations in color, but might severely fail for unseen variations in lighting, spatial position and backgrounds. Our results show that we need new RGB-D models that can exploit the best of both modalities while remaining robust to changes in a single modality.

Event-based cameras do not capture frames like an RGB camera, only data from pixels that detect a change in light intensity, making it a better alternative for processing videos. The sparse data acquired from event-based video only captures movement in an asynchronous way. In this paper an evaluation is made on the efficiency and accuracy of object detection, specifically localization, between sparse and dense representations of data. Convolutional Neural Networks are used to train and test on images and event-based data. The results show a positive trade-off in terms of accuracy and efficiency for using sparse event-based data instead of dense data like images. These results provide a basis for an argument to use event-based cameras instead of RGB cameras when dealing with object detection.