Scanline Resolution-Invariant Depth Completion Using a Single Image and Sparse LiDAR Point Cloud

Ryu, Kwonyoung; Lee, Kang-il; Cho, Jegyeong; Yoon, Kuk-Jin

doi:10.1109/lra.2021.3096499

Cited by 12 publications

(3 citation statements)

References 23 publications

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Section: Gradient-related Methodsmentioning

confidence: 99%

“…Gradient information has been used in previous depth completion works, such as [45][46][47][48][49][50]. Commonly, there are two ways to introduce gradient information into deep networks: 1) incorporating gradients into the model to guide depth completion [45], and 2) introducing gradients into the loss for constraints [45][46][47][48][49][50]. Specifically, Hwang et al [45] designed a teacher network to learn gradient depth images, which were then used to train their geometrical edge CNN through a Knowledge-Distillation loss function.…”

Section: Gradient-related Methodsmentioning

confidence: 99%

“…As a parametric method, their gradient generation method required more computing resources. Nguyen et al [ 46 ] and Ryu et al [ 47 ] adopted a gradient-related loss to encourage local smoothness of depth predictions. Gu et al [ 48 ] used some pseudo depth maps to rectify input sparse depth, and supervised training by some structural losses including gradient loss.…”

Section: Related Workmentioning

confidence: 99%

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SPNet: Structure preserving network for depth completion

Luo

Fan

et al. 2023

PLoS ONE

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Depth completion aims to predict a dense depth map from a sparse one. Benefiting from the powerful ability of convolutional neural networks, recent depth completion methods have achieved remarkable performance. However, it is still a challenging problem to well preserve accurate depth structures, such as tiny structures and object boundaries. To tackle this problem, we propose a structure preserving network (SPNet) in this paper. Firstly, an efficient multi-scale gradient extractor (MSGE) is proposed to extract useful multi-scale gradient images, which contain rich structural information that is helpful in recovering accurate depth. The MSGE is constructed based on the proposed semi-fixed depthwise separable convolution. Meanwhile, we adopt a stable gradient MAE loss (LGMAE) to provide additional depth gradient constrain for better structure reconstruction. Moreover, a multi-level feature fusion module (MFFM) is proposed to adaptively fuse the spatial details from low-level encoder and the semantic information from high-level decoder, which will incorporate more structural details into the depth modality. As demonstrated by experiments on NYUv2 and KITTI datasets, our method outperforms some state-of-the-art methods in terms of both quantitative and quantitative evaluations.

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