Dilated U-Block for Lightweight Indoor Depth Completion With Sobel Edge

Tao, Zhao; Pan, Shuguo; Zhang, Hui; Sun, Yingchun

doi:10.1109/lsp.2021.3092280

Cited by 13 publications

(4 citation statements)

References 29 publications

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“…In deep learning-based image tasks, Xu et al [42] proposed that the convolutional neural network tends to focus more on learning low-frequency content rather than high-frequency information in the image. The pixel-level image task has higher requirements on the learning ability of high-frequency regions such as edge contours [20,24]. Liu etal.…”

Section: Edge Information For Pixel-wise Image Tasksmentioning

confidence: 99%

“…This mechanism has proven to be particularly effective for the depth completion task [19-21, 39, 41, 46]. Meanwhile, the attention to edge information in the network prediction process can better generate a clear boundary and complete depth map [23,24]. In UDA depth estimation, we attempt to incorporate selfattention guided by edge information into the network to address the problem of depth map missing holes.…”

Section: Edge-guided Self-attention Mechanismmentioning

confidence: 99%

“…Some research has shown that the self-attention mechanism is conducive to discovering contextual connections in a single image, making it useful for pixelwise image processing such as depth estimation [19][20][21][22]. Edge constraints can also preserve more shape semantic information on the basis of geometric constraints [20,[23][24][25], which can effectively improve the quality of predicted depth maps. herefore, we make full use of these two features: First, in the depth estimation network task of a single domain, we insert a self-attention mechanism module to enhance the pixel-wise feature extraction capability of the network and strengthen the pixel correlation of a single image, filling depth missing holes [26].…”

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: Outdoor UDA Depth Estimation with Intra-domain Attention and Cross-domain Edge Constraint

Peng,

Pan,

et al. 2023

Preprint

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In the unsupervised domain adaptation (UDA) depth estimation task, a new adaptive approach is to use the bidirectional transformation network to transfer the style between the target and source domain inputs, and then train the depth estimation network in their respective domains. However, the domain adaptation process and the style transfer may result in defects and biases.Especially in outdoor environments with long distances and complex surroundings, it often leads to map holes and missing areas in the target domain's depth output. To address these issues, We propose a training network that has been improved in terms of model structure and supervision constraints. First, we introduce a edge-guided self-attention mechanism in the task network of each domain to enhance the network's attention to high-frequency edge features, maintain clear boundaries and fill in missing areas of depth. Furthermore, we utilize an edge detection algorithm to extract edge features from the input of the target domain. Then we establish edge consistency constraints between inter-domain entities in order to narrow the gap between domains and make domain-to-domain transfers easier. Our experimental demonstrate that our proposed method effectively solve the aforementioned problem, resulting in a higher quality depth map and outperforming existing state-of-the-art methods.Our code and datasets are all open-sourced at the following address:https://github.com/chongagp/UDA-depth-estimation-under-intra-domain-self-attention-guidance-and-cross-domain-edge-constraints

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Section: Edge Information For Pixel-wise Image Tasksmentioning

confidence: 99%

Section: Edge-guided Self-attention Mechanismmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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WITHDRAWN: Outdoor UDA Depth Estimation with Intra-domain Attention and Cross-domain Edge Constraint

Peng,

Pan,

et al. 2023

Preprint

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“…To overcome this limitation, lightweight networks for depth completion tasks were proposed. Tao et al introduced lightweight depth completion with a Sobel edge prediction network [ 11 ] and self-attention-based multi-level feature integration and extraction [ 12 ]. Although these approaches contribute to decreasing the computational cost by effectively reducing the parameter size and model complexity, they cannot leverage or surpass the better performance of existing networks.…”

Section: Introductionmentioning

confidence: 99%

Lightweight Depth Completion Network with Local Similarity-Preserving Knowledge Distillation

Jeong

Park

Cho

et al. 2022

Sensors

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Depth perception capability is one of the essential requirements for various autonomous driving platforms. However, accurate depth estimation in a real-world setting is still a challenging problem due to high computational costs. In this paper, we propose a lightweight depth completion network for depth perception in real-world environments. To effectively transfer a teacher’s knowledge, useful for the depth completion, we introduce local similarity-preserving knowledge distillation (LSPKD), which allows similarities between local neighbors to be transferred during the distillation. With our LSPKD, a lightweight student network is precisely guided by a heavy teacher network, regardless of the density of the ground-truth data. Experimental results demonstrate that our method is effective to reduce computational costs during both training and inference stages while achieving superior performance over other lightweight networks.

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Deep Cost Ray Fusion for Sparse Depth Video Completion

Kim,

Park

et al. 2024

Lecture Notes in Computer Science

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Dilated U-Block for Lightweight Indoor Depth Completion With Sobel Edge

Cited by 13 publications

References 29 publications

WITHDRAWN: Outdoor UDA Depth Estimation with Intra-domain Attention and Cross-domain Edge Constraint

WITHDRAWN: Outdoor UDA Depth Estimation with Intra-domain Attention and Cross-domain Edge Constraint

Lightweight Depth Completion Network with Local Similarity-Preserving Knowledge Distillation

Deep Cost Ray Fusion for Sparse Depth Video Completion

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