Convolutional hypercube pyramid for accurate RGB-D object category and instance recognition

Deep learning based methods have achieved unprecedented success in solving several computer vision problems involving RGB images. However, this level of success is yet to be seen on RGB-D images owing to two major challenges in this domain: training data deficiency and multi-modality input dissimilarity. We present an RGB-D object recognition framework that addresses these two key challenges by effectively embedding depth and point cloud data into the RGB domain. We employ a convolutional neural network (CNN) pre-trained on RGB data as a feature extractor for both color and depth channels and propose a rich coarse-to-fine feature representation scheme, coined Hypercube Pyramid, that is able to capture discriminatory information at different levels of detail. Finally, we present a novel fusion scheme to combine the Hypercube Pyramid features with the activations of fully connected neurons to construct a compact representation prior to classification. By employing Extreme Learning Machines (ELM) as non-linear classifiers, we show that the proposed method outperforms ten state-of-the-art algorithms for several tasks in terms of recognition accuracy on the benchmark Washington RGB-D and 2D3D object datasets by a large margin (upto 50% reduction in error rate)