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 10 publications

(5 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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“…For instance, Liu et al [27] added an edge loss term to the overall loss function. Hegde et al [28] proposed gradient-aware mean-squared error loss (GAMSE) that effectively harnesses edge information. The second approach involves integrating the guiding information into the model itself [29], meaning incorporating the guiding information during the model's inference process.…”

Section: Related Workmentioning

confidence: 99%

Depth Information Precise Completion-GAN: A Precisely Guided Method for Completing Ill Regions in Depth Maps

et al. 2023

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In the depth map obtained through binocular stereo matching, there are many ill regions due to reasons such as lighting or occlusion. These ill regions cannot be accurately obtained due to the lack of information required for matching. Since the completion model based on Gan generates random results, it cannot accurately complete the depth map. Therefore, it is necessary to accurately complete the depth map according to reality. To address this issue, this paper proposes a depth information precise completion GAN (DIPC-GAN) that effectively uses the Guid layer normalization (GuidLN) module to guide the model for precise completion by utilizing depth edges. GuidLN flexibly adjusts the weights of the guiding conditions based on intermediate results, allowing modules to accurately and effectively incorporate the guiding information. The model employs multiscale discriminators to discriminate results of different resolutions at different generator stages, enhancing the generator’s grasp of overall image and detail information. Additionally, this paper proposes Attention-ResBlock, which enables all ResBlocks in each task module of the GAN-based multitask model to focus on their own task by sharing a mask. Even when the ill regions are large, the model can effectively complement the missing details in these regions. Additionally, the multiscale discriminator in the model enhances the generator’s robustness. Finally, the proposed task-specific residual module can effectively focus different subnetworks of a multitask model on their respective tasks. The model has shown good repair results on datasets, including artificial, real, and remote sensing images. The final experimental results showed that the model’s REL and RMSE decreased by 9.3% and 9.7%, respectively, compared to RDFGan.

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DeepDNet: Deep Dense Network for Depth Completion Task

Cited by 10 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

Depth Information Precise Completion-GAN: A Precisely Guided Method for Completing Ill Regions in Depth Maps

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