正二十面体の折り紙王冠表現による複数魚眼画像からの360度深度推定
360$^\circ$ Depth Estimation from Multiple Fisheye Images with Origami Crown Representation of Icosahedron
複数の全方位画像から屋内環境の全周深度を推定する手法を提案し、正二十面体ベースの表現とConvNet（CrownConv）および球面スイープで高精度かつ高速な推定を実現した。
著者: Ren Komatsu, Hiromitsu Fujii, Yusuke Tamura, Atsushi Yamashita, Hajime Asama
分類: cs.CV, cs.RO
原文アブストラクト
In this study, we present a method for all-around depth estimation from multiple omnidirectional images for indoor environments. In particular, we focus on plane-sweeping stereo as the method for depth estimation from the images. We propose a new icosahedron-based representation and ConvNets for omnidirectional images, which we name "CrownConv" because the representation resembles a crown made of origami. CrownConv can be applied to both fisheye images and equirectangular images to extract features. Furthermore, we propose icosahedron-based spherical sweeping for generating the cost volume on an icosahedron from the extracted features. The cost volume is regularized using the three-dimensional CrownConv, and the final depth is obtained by depth regression from the cost volume. Our proposed method is robust to camera alignments by using the extrinsic camera parameters; therefore, it can achieve precise depth estimation even when the camera alignment differs from that in the training dataset. We evaluate the proposed model on synthetic datasets and demonstrate its effectiveness. As our proposed method is computationally efficient, the depth is estimated from four fisheye images in less than a second using a laptop with a GPU. Therefore, it is suitable for real-world robotics applications. Our source code is available at https://github.com/matsuren/crownconv360depth.
