Results 301 to 310 of about 4,616,264 (348)

Usage of needle maps and shadows to overcome depth edges in depth map reconstruction

2008 19th International Conference on Pattern Recognition, 2008
Photometric stereo is a method of recovering surface normals (needle map) from images. The surface integral of surface normals is used to reconstruct a depth map; however, the depth edges, which are discontinuous boundaries of the depth map, pose a problem for photometric stereo. When the surface of objects includes depth edges, the reconstructed depth
Masaaki Iiyama, Koki Hamada, Koh Kakusho, Michihiko Minoh   +3 more
openaire   +1 more source

Multi-Direction Dictionary Learning Based Depth Map Super-Resolution With Autoregressive Modeling

IEEE transactions on multimedia, 2020
3D depth cameras have become more and more popular in recent years. However, depth maps captured by these cameras can hardly be used in 3D reconstruction directly because they often suffer from low resolution and blurring depth discontinuities.
Jin Wang, W. Xu, Jian-Feng Cai, Qing Zhu, Yunhui Shi, Baocai Yin   +5 more
semanticscholar   +1 more source

Monocular Depth Estimation Using Relative Depth Maps

2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR), 2019
We propose a novel algorithm for monocular depth estimation using relative depth maps. First, using a convolutional neural network, we estimate relative depths between pairs of regions, as well as ordinary depths, at various scales. Second, we restore relative depth maps from selectively estimated data based on the rank-1 property of pairwise ...
Jaehan Lee, Chang-Su Kim 0001
openaire   +1 more source

Depth map enhancement based on color and depth consistency

The Visual Computer, 2013
Current low-cost depth sensing techniques, such as Microsoft Kinect, still can achieve only limited precision. The resultant depth maps are often found to be noisy, misaligned with the color images, and even contain many large holes. These limitations make it difficult to be adopted by many graphics applications.
Yanke Wang, Fan Zhong, Qunsheng Peng 0001, Xueying Qin   +3 more
openaire   +1 more source

Densely Connecting Depth Maps for Monocular Depth Estimation

2020
Predicting depth map from a single RGB image is beneficial for many three-dimensional applications. Although recent monocular depth estimation methods have achieved impressive accuracy, the preference on high-level features or low-level features prevents them from balancing sharpness and fidelity of depth maps.
Jinqing Zhang, Haosong Yue, Xingming Wu, Weihai Chen, Changyun Wen   +4 more
openaire   +1 more source

Depth map generation by image classification

SPIE Proceedings, 2004
This paper presents a novel and fully automatic technique to estimate depth information from a single input image. The proposed method is based on a new image classification technique able to classify digital images (also in Bayer pattern format) as indoor, outdoor with geometric elements or outdoor without geometric elements.
Sebastiano Battiato, Salvatore Curti, Marco La Cascia, Marcello Tortora, Emiliano Scordato   +4 more
openaire   +1 more source

Residual dense network for intensity-guided depth map enhancement

Information Sciences, 2019
The depth maps captured by sensors always suffer from low resolution and random noise. Recently, by introducing the guidance from the color image, deep convolutional neural network (DCNN) shows significant improvements for depth map enhancement. However,
Y. Zuo, Yuming Fang, Yong Yang, Xiwu Shang, Bin Wang   +4 more
semanticscholar   +1 more source

Pyramid-Structured Depth MAP Super-Resolution Based on Deep Dense-Residual Network

IEEE Signal Processing Letters, 2019
Although deep convolutional neural networks (DCNN) show significant improvement for single depth map (SD) super-resolution (SR) over the traditional counterparts, most SDSR DCNNs do not reuse the hierarchical features for depth map SR resulting in ...
Liqin Huang, Jianjia Zhang, Y. Zuo, Qiang Wu   +3 more
semanticscholar   +1 more source

Region-based depth recovery for highly sparse depth maps

2017 IEEE International Conference on Image Processing (ICIP), 2017
The accurate recovery of missing values in depth maps is an important problem in computer vision and image processing. In depth maps with large, irregular missing regions (i.e., sparse depth maps) inaccuracies arise when depth values of known pixels are used to recover depth near object edges and depth discontinuities (leakage).
Said Pertuz, Joni-Kristian Kämäräinen
openaire   +1 more source

Occlusion-Aware Depth Map Coding Optimization Using Allowable Depth Map Distortions

IEEE Transactions on Image Processing, 2019
In depth map coding, rate-distortion optimization for those pixels that will cause occlusion in view synthesis is a rather challenging task, since the synthesis distortion estimation is complicated by the warping competition and the occlusion order can be easily changed by the adopted optimization strategy.
Pan Gao 0001, Aljosa Smolic
openaire   +2 more sources