Monocular Depth Estimation with Affinity, Vertical Pooling, and Label Enhancement
Autor: | Gan Yukang, Liang Lin, Xiangyu Xu, Wenxiu Sun |
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Rok vydání: | 2018 |
Předmět: |
050210 logistics & transportation
Ground truth Monocular Pixel business.industry Computer science 05 social sciences Pooling Pattern recognition 02 engineering and technology Convolutional neural network Image (mathematics) Feature (computer vision) 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Artificial intelligence business |
Zdroj: | Computer Vision – ECCV 2018 ISBN: 9783030012182 ECCV (3) |
Popis: | Significant progress has been made in monocular depth estimation with Convolutional Neural Networks (CNNs). While absolute features, such as edges and textures, could be effectively extracted, the depth constraint of neighboring pixels, namely relative features, has been mostly ignored by recent CNN-based methods. To overcome this limitation, we explicitly model the relationships of different image locations with an affinity layer and combine absolute and relative features in an end-to-end network. In addition, we consider prior knowledge that major depth changes lie in the vertical direction, and thus, it is beneficial to capture long-range vertical features for refined depth estimation. In the proposed algorithm we introduce vertical pooling to aggregate image features vertically to improve the depth accuracy. Furthermore, since the Lidar depth ground truth is quite sparse, we enhance the depth labels by generating high-quality dense depth maps with off-the-shelf stereo matching method taking left-right image pairs as input. We also integrate multi-scale structure in our network to obtain global understanding of the image depth and exploit residual learning to help depth refinement. We demonstrate that the proposed algorithm performs favorably against state-of-the-art methods both qualitatively and quantitatively on the KITTI driving dataset. |
Databáze: | OpenAIRE |
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