Autor: |
Choi, Jaehoon, Jung, Dongki, Lee, Yonghan, Kim, Deokhwa, Manocha, Dinesh, Lee, Donghwan |
Rok vydání: |
2020 |
Předmět: |
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Druh dokumentu: |
Working Paper |
Popis: |
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. |
Databáze: |
arXiv |
Externí odkaz: |
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