Learning Hierarchical Color Guidance for Depth Map Super-Resolution

• URL: http://arxiv.org/abs/2403.07290v1
• Date: Tue, 12 Mar 2024 03:44:46 GMT
• Title: Learning Hierarchical Color Guidance for Depth Map Super-Resolution
• Authors: Runmin Cong, Ronghui Sheng, Hao Wu, Yulan Guo, Yunchao Wei, Wangmeng Zuo, Yao Zhao, and Sam Kwong
• Abstract summary: We propose a hierarchical color guidance network to achieve depth map super-resolution (DSR) On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features. On the other hand, the high-level abstract guidance module is proposed to maintain semantic consistency in the reconstruction process.
• Score: 168.1463802622881
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: Color information is the most commonly used prior knowledge for depth map super-resolution (DSR), which can provide high-frequency boundary guidance for detail restoration. However, its role and functionality in DSR have not been fully developed. In this paper, we rethink the utilization of color information and propose a hierarchical color guidance network to achieve DSR. On the one hand, the low-level detail embedding module is designed to supplement high-frequency color information of depth features in a residual mask manner at the low-level stages. On the other hand, the high-level abstract guidance module is proposed to maintain semantic consistency in the reconstruction process by using a semantic mask that encodes the global guidance information. The color information of these two dimensions plays a role in the front and back ends of the attention-based feature projection (AFP) module in a more comprehensive form. Simultaneously, the AFP module integrates the multi-scale content enhancement block and adaptive attention projection block to make full use of multi-scale information and adaptively project critical restoration information in an attention manner for DSR. Compared with the state-of-the-art methods on four benchmark datasets, our method achieves more competitive performance both qualitatively and quantitatively.

Related papers

• Symmetric Uncertainty-Aware Feature Transmission for Depth Super-Resolution [52.582632746409665]
  We propose a novel Symmetric Uncertainty-aware Feature Transmission (SUFT) for color-guided DSR. Our method achieves superior performance compared to state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-06-01T06:35:59Z)
• Towards Reliable Image Outpainting: Learning Structure-Aware Multimodal Fusion with Depth Guidance [49.94504248096527]
  We propose a Depth-Guided Outpainting Network (DGONet) to model the feature representations of different modalities. Two components are designed to implement: 1) The Multimodal Learning Module produces unique depth and RGB feature representations from perspectives of different modal characteristics. We specially design an additional constraint strategy consisting of Cross-modal Loss and Edge Loss to enhance ambiguous contours and expedite reliable content generation.
  arXiv  Detail & Related papers  (2022-04-12T06:06:50Z)
• Cross-modality Discrepant Interaction Network for RGB-D Salient Object Detection [78.47767202232298]
  We propose a novel Cross-modality Discrepant Interaction Network (CDINet) for RGB-D SOD. Two components are designed to implement the effective cross-modality interaction. Our network outperforms $15$ state-of-the-art methods both quantitatively and qualitatively.
  arXiv  Detail & Related papers  (2021-08-04T11:24:42Z)
• BridgeNet: A Joint Learning Network of Depth Map Super-Resolution and Monocular Depth Estimation [60.34562823470874]
  We propose a joint learning network of depth map super-resolution (DSR) and monocular depth estimation (MDE) without introducing additional supervision labels. One is the high-frequency attention bridge (HABdg) designed for the feature encoding process, which learns the high-frequency information of the MDE task to guide the DSR task. The other is the content guidance bridge (CGBdg) designed for the depth map reconstruction process, which provides the content guidance learned from DSR task for MDE task.
  arXiv  Detail & Related papers  (2021-07-27T01:28:23Z)
• High-resolution Depth Maps Imaging via Attention-based Hierarchical Multi-modal Fusion [84.24973877109181]
  We propose a novel attention-based hierarchical multi-modal fusion network for guided DSR. We show that our approach outperforms state-of-the-art methods in terms of reconstruction accuracy, running speed and memory efficiency.
  arXiv  Detail & Related papers  (2021-04-04T03:28:33Z)
• Fast Generation of High Fidelity RGB-D Images by Deep-Learning with Adaptive Convolution [10.085742605397124]
  We propose a deep-learning based approach to efficiently generate RGB-D images with completed information in high resolution. As an end-to-end approach, high fidelity RGB-D images can be generated efficiently at the rate of around 21 frames per second.
  arXiv  Detail & Related papers  (2020-02-12T16:14:38Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.