Results 11 to 20 of about 25,036,571 (294)
Semantic RGB-D Image Synthesis [PDF]
2023 IEEE/CVF International Conference on Computer Vision Workshops (ICCVW), 2023 ICCV Workshop on Representation Learning with Very Limited Images ...
Shijie Li 0006, Rong Li, Juergen Gall
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Real-time RGB-D image stitching using multiple Kinects for improved field of view
International Journal of Advanced Robotic Systems, 2017 This article concerns the problems of a defective depth map and limited field of view of Kinect-style RGB-D sensors. An anisotropic diffusion based hole-filling method is proposed to recover invalid depth data in the depth map.
Hengyu Li +6 more
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RGB-D SLAM with Manhattan Frame Estimation Using Orientation Relevance
Sensors, 2019 Due to image noise, image blur, and inconsistency between depth data and color image, the accuracy and robustness of the pairwise spatial transformation computed by matching extracted features of detected key points in existing sparse Red Green Blue ...
Liang Wang, Zhiqiu Wu
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Deep Depth Completion of a Single RGB-D Image [PDF]
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition, 2018 Accepted by CVPR2018 (Spotlight). Project webpage: http://deepcompletion.cs.princeton.edu/ This version includes supplementary materials which provide more implementation details, quantitative evaluation, and qualitative results.
Yinda Zhang 0001, Thomas A. Funkhouser
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Unsupervised Feature Learning for RGB-D Image Classification [PDF]
, 2015 Motivated by the success of Deep Neural Networks in computer vision, we propose a deep Regularized Reconstruction Independent Component Analysis network (R\(^2\)ICA) for RGB-D image classification. In each layer of this network, we include a R\(^2\)ICA as the basic building block to determine the relationship between the gray-scale and depth images ...
I-Hong Jhuo +4 more
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Proceedings of the 20th ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games, 2016 This paper presents an image-based rendering (IBR) system based on RGB-D images. The input of our system consists of RGB-D images captured at sparse locations in the scene and can be expanded by adding new RGB-D images. The sparsity of RGB-D images increases the usability of our system as the user need not capture a RGB-D image stream in a single shot,
Yeongyu Jeong +4 more
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Shadow Removal from Single RGB-D Images
2014 IEEE Conference on Computer Vision and Pattern Recognition, 2014 We present the first automatic method to remove shadows from single RGB-D images. Using normal cues directly derived from depth, we can remove hard and soft shadows while preserving surface texture and shading. Our key assumption is: pixels with similar normals, spatial locations and chromaticity should have similar colors. A modified nonlocal matching
Yao Xiao +2 more
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RGB-D-Fusion: Image Conditioned Depth Diffusion of Humanoid Subjects [PDF]
, 2023 We present RGB-D-Fusion, a multi-modal conditional denoising diffusion probabilistic model to generate high resolution depth maps from low-resolution monocular RGB images of humanoid subjects. RGB-D-Fusion first generates a low-resolution depth map using
Kirch, Sascha +13 more
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GrapesNet: Indian Grape Clusters RGB & RGB-D Image Datasets
, 2023 There are four different RGB and RGB-D image datasets of grape clusters created in Indian vineyards are proposed in the GrapesNet: 1. Single grape cluster per image with artificial background and single grape cluster per image with real background 2 ...
Barbole, Dhanashree +1 more
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Robust Image Matching Based on Image Feature and Depth Information Fusion
Machines, 2022 In this paper, we propose a robust image feature extraction and fusion method to effectively fuse image feature and depth information and improve the registration accuracy of RGB-D images.
Zhiqiang Yan +3 more
doaj +1 more source