DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion

• URL: http://arxiv.org/abs/2211.10994v1
• Date: Sun, 20 Nov 2022 14:56:18 GMT
• Title: DesNet: Decomposed Scale-Consistent Network for Unsupervised Depth Completion
• Authors: Zhiqiang Yan and Kun Wang and Xiang Li and Zhenyu Zhang and Jun Li and Jian Yang
• Abstract summary: Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. We propose the scale-consistent learning (DSCL) strategy, which disintegrates the absolute depth into relative depth prediction and global scale estimation. Our approach achieves state-of-the-art performance on indoor NYUv2 dataset.
• Score: 28.91716162403531
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Unsupervised depth completion aims to recover dense depth from the sparse one without using the ground-truth annotation. Although depth measurement obtained from LiDAR is usually sparse, it contains valid and real distance information, i.e., scale-consistent absolute depth values. Meanwhile, scale-agnostic counterparts seek to estimate relative depth and have achieved impressive performance. To leverage both the inherent characteristics, we thus suggest to model scale-consistent depth upon unsupervised scale-agnostic frameworks. Specifically, we propose the decomposed scale-consistent learning (DSCL) strategy, which disintegrates the absolute depth into relative depth prediction and global scale estimation, contributing to individual learning benefits. But unfortunately, most existing unsupervised scale-agnostic frameworks heavily suffer from depth holes due to the extremely sparse depth input and weak supervised signal. To tackle this issue, we introduce the global depth guidance (GDG) module, which attentively propagates dense depth reference into the sparse target via novel dense-to-sparse attention. Extensive experiments show the superiority of our method on outdoor KITTI benchmark, ranking 1st and outperforming the best KBNet more than 12% in RMSE. In addition, our approach achieves state-of-the-art performance on indoor NYUv2 dataset.

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