DeepDNet: Deep Dense Network for Depth Completion Task

Hegde, Girish; Pharale, Tushar; Jahagirdar, Soumya; Nargund, Vaishakh; Tabib, Ramesh Ashok; Mudenagudi, Uma; Vandrotti, Basavaraja Shanthappa; Dhiman, Ankit

doi:10.1109/cvprw53098.2021.00248

Cited by 7 publications

(4 citation statements)

References 12 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%

“…Liu et al [49] applied a constraint on depth gradient to penalize the disagreement of depth boundaries. Hegde et al [50] proposed a novel Gradient Aware Mean Squared Error Loss (GAMSE) to preserve boundary information in predicted dense depth maps.…”

Section: Gradient-related Methodsmentioning

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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Section: Gradient-related Methodsmentioning

confidence: 99%

Section: Gradient-related Methodsmentioning

confidence: 99%

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SPNet: Structure preserving network for depth completion

Luo

Fan

et al. 2023

PLoS ONE

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“…Our method takes an RGB image as input and predicts dense surface normals and occlusion boundaries to solve the problem of missing pixels in the original observation. Hegde et al [ 23 ] utilized an exact sparse depth as input to the RGB image to generate a dense depth map. The method focuses on a quadtree decomposition modeling approach.…”

Section: Related Workmentioning

confidence: 99%

Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net): Polarization Image Demosaicing Based on Stokes Vector Completion in Low-Light Environment

Chen,

Hao,

Duan

et al. 2024

Sensors

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Polarization imaging has achieved a wide range of applications in military and civilian fields such as camouflage detection and autonomous driving. However, when the imaging environment involves a low-light condition, the number of photons is low and the photon transmittance of the conventional Division-of-Focal-Plane (DoFP) structure is small. Therefore, the traditional demosaicing methods are often used to deal with the serious noise and distortion generated by polarization demosaicing in low-light environment. Based on the aforementioned issues, this paper proposes a model called Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net) for simulating a sparse polarization sensor acquisition of polarization images in low-light environments. The model consists of two parts: an intensity image enhancement network and a Stokes vector complementation network. In this work, the intensity image enhancement network is used to enhance low-light images and obtain high-quality RGB images, while the Stokes vector is used to complement the network. We discard the traditional idea of polarization intensity image interpolation and instead design a polarization demosaicing method with Stokes vector complementation. By using the enhanced intensity image as a guide, the completion of the Stokes vector is achieved. In addition, to train our network, we collected a dataset of paired color polarization images that includes both low-light and regular-light conditions. A comparison with state-of-the-art methods on both self-constructed and publicly available datasets reveals that our model outperforms traditional low-light image enhancement demosaicing methods in both qualitative and quantitative experiments.

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MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification

Bala

Hossain

et al. 2023

Neural Networks

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DeepDNet: Deep Dense Network for Depth Completion Task

Cited by 7 publications

References 12 publications

SPNet: Structure preserving network for depth completion

SPNet: Structure preserving network for depth completion

Low-Light Sparse Polarization Demosaicing Network (LLSPD-Net): Polarization Image Demosaicing Based on Stokes Vector Completion in Low-Light Environment

MonkeyNet: A robust deep convolutional neural network for monkeypox disease detection and classification

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