Results 251 to 260 of about 4,496,548 (308)

Mapping with depth panoramas

2015 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), 2015
This work demonstrates the use of depth panoramas in the construction of detailed 3D models of extended environments. The paper describes an approach to the acquisition of such panoramic images using a robotic platform that collects sequences of depth images with a commodity depth sensor.
Camillo J. Taylor, Anthony Cowley, Rafe Kettler, Kai Ninomiya, Mayank Gupta, Boyang Niu   +5 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.
BATTIATO, SEBASTIANO, S. CURTI, M. LA CASCIA, E. SCORDATO, M. TORTORA   +4 more
openaire   +1 more source

Robust Color Guided Depth Map Restoration

IEEE Transactions on Image Processing, 2017
One of the most challenging issues in color guided depth map restoration is the inconsistency between color edges in guidance color images and depth discontinuities on depth maps. This makes the restored depth map suffer from texture copy artifacts and blurring depth discontinuities.
Wei Liu, Xiaogang Chen, Jie Yang, Qiang Wu   +3 more
openaire   +3 more sources

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

Computing depth maps from descent imagery

Proceedings of the 2001 IEEE Computer Society Conference on Computer Vision and Pattern Recognition. CVPR 2001, 2005
In the exploration of the planets of our solar system, images taken during a lander's descent to the surface of a planet provide a critical link between orbital images and surface images. The descent images not only allow us to locate the landing site in a global coordinate frame, but they also provide progressively higher-resolution maps for mission ...
Yalin Xiong, Clark F. Olson, Larry H. Matthies   +2 more
openaire   +1 more source

Depth Map Estimation Using Defocus and Motion Cues

IEEE transactions on circuits and systems for video technology (Print), 2019
Significant recent developments in 3D display technology have focused on techniques for converting 2D media into 3D. Depth map is an integral part of 2D-to-3D conversion.
Himanshu Kumar, Ajeet Singh Yadav, Sumana Gupta, K. Venkatesh   +3 more
semanticscholar   +1 more source

Depth map generation based on depth from focus

2010 International Conference on Electronic Devices, Systems and Applications, 2010
The key step in 3D data generation for future 3D display system is the calculation of depth map which is a gray-level image of exactly the same size of the original 2D image data that indicates the relative distance of each pixel from the camera to the object in real world. This paper presents a practical approach to generate depth map from a series of
Yi-Che Chen, null Yi-Chin Wu, null Chih-Hung Liu, null Wei-Chih Sun, Yung-Chang Chen   +4 more
openaire   +1 more source

Depth upsampling methods for high resolution depth map

2018 International Conference on Electronics, Information, and Communication (ICEIC), 2018
A depth camera measures depth information of the object using a structured light or a time-of-flight method. However, those methods have a problem that the resolution of the depth map is small. This problem affects a generation of high-quality three-dimensional (3D) video contents.
Yong-Jun Chang, Sunho Kim, Yo-Sung Ho
openaire   +1 more source

Generative Adversarial Networks for Depth Map Estimation from RGB Video

2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW), 2018
Depth cues are essential to achieving high-level scene understanding, and in particular to determining geometric relations between objects. The ability to reason about depth information in scene analysis tasks can often result in improved decision-making
Kin Gwn Lore, K. Reddy, M. Giering, Edgar A. Bernal   +3 more
semanticscholar   +1 more source