SelfDeco: 困難な屋内環境における自己教師あり単眼深度補完
SelfDeco: Self-Supervised Monocular Depth Completion in Challenging Indoor Environments
疎な深度計測と単眼動画のみを用いた自己教師あり学習により、テクスチャのない領域や反射・透明面を含む屋内環境で高精度に深度を補完する手法を提案した。
著者: Jaehoon Choi, Dongki Jung, Yonghan Lee, Deokhwa Kim, Dinesh Manocha, Donghwan Lee
分類: cs.CV, cs.RO
原文アブストラクト
We present a novel algorithm for self-supervised monocular depth completion. Our approach is based on training a neural network that requires only sparse depth measurements and corresponding monocular video sequences without dense depth labels. Our self-supervised algorithm is designed for challenging indoor environments with textureless regions, glossy and transparent surface, non-Lambertian surfaces, moving people, longer and diverse depth ranges and scenes captured by complex ego-motions. Our novel architecture leverages both deep stacks of sparse convolution blocks to extract sparse depth features and pixel-adaptive convolutions to fuse image and depth features. We compare with existing approaches in NYUv2, KITTI, and NAVERLABS indoor datasets, and observe 5-34 % improvements in root-means-square error (RMSE) reduction.
