High-accuracy stereo depth maps using structured light

A Taxonomy and Evaluation of Dense Two-Frame Stereo Correspondence Algorithms

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Active, optical range imaging sensors

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Recent progress in coded structured light as a technique to solve the correspondence problem

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Computing rectifying homographies for stereo vision

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Surfaces from stereo: integrating feature matching, disparity estimation, and contour detection

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Spacetime stereo: shape recovery for dynamic scenes

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Computer Description of Curved Objects

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Range image acquisition with a single binary-encoded light pattern

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Better optical triangulation through spacetime analysis

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3D photography on your desk

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Solving Stereo Matching Problems Using Interior Point Methods

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Methods for depth-map filtering in view-plus-depth 3D video representation

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Feasibility Boundary in Dense and Semi-Dense Stereo Matching

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Disparity and optical flow partitioning using extended Potts priors

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High‐Quality Stereo Depth Map Generation Using Infrared Pattern Projection

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Even More Confident Predictions with Deep Machine-Learning

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Depth variation and stereo processing tasks in natural scenes

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Autonomous Robot-Guided Inspection System Based on Offline Programming and RGB-D Model

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Single-Shot Monocular RGB-D Imaging Using Uneven Double Refraction

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A novel image dehazing framework for robust vision‐based intelligent systems

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A Refractive Stereo Structured-Light 3-D Measurement System for Immersed Object

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A Methodology for Visually Lossless JPEG2000 Compression of Monochrome Stereo Images

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FPGA design of the computation unit for the semi-global stereo matching algorithm

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Real-Time and High-Resolution 3D Face Measurement via a Smart Active Optical Sensor

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Joint disparity and variable size-block optimization algorithm for stereoscopic image compression

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Stereo Vision for Obstacle Detection: A Graph-Based Approach

Foggia, P., Jolion, J.-M., Limongiello, A., & Vento, M. (2007) · Lecture notes in computer science · doi:10.1007/978-3-540-72903-7_4 · Cited by 15

Semantic Single-Image DehazingPreprint

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Shape-reserved stereo matching with segment-based cost aggregation and dual-path refinement

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Fast depth estimation on mobile platforms and FPGA devices

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A Study in 3D-Reconstruction Using Kinect Sensor

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High-precision stereo disparity estimation using HMMF models

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Quantifying Levodopa-Induced Dyskinesia Using Depth Camera

Dyshel, M., Arkadir, D., Bergman, H., & Weinshall, D. (2015) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw.2015.73 · Cited by 14

Linear Recursive Non-Local Edge-Aware Filter

Bu, P., Zhao, H., Jin, Y., & Ma, Y. (2020) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2020.3015840 · Cited by 14

Depth Super-Resolution via Deep Controllable Slicing Network

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Depth Map Refinement for Immersive Video

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FOODCAM: A Novel Structured Light-Stereo Imaging System for Food Portion Size Estimation

Raju, V. B., & Sazonov, E. E. (2022) · Sensors · doi:10.3390/s22093300 · Cited by 14

A 3D Imaging Framework Based on High-Resolution Photometric-Stereo and Low-Resolution Depth

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Compressive Estimation and Imaging Based on Autoregressive Models

Testa, M., & Magli, E. (2016) · IEEE Transactions on Image Processing · doi:10.1109/tip.2016.2601444 · Cited by 14

Robust Template-Based Watermarking for DIBR 3D Images

Kim, W.-H., Hou, J.-U., Jang, H.-U., & Lee, H.-K. (2018) · Applied Sciences · doi:10.3390/app8060911 · Cited by 14

Digital inspection

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3D depth estimation for visual inspection using wavelet transform modulus maxima

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Toward Global Minimum through Combined Local Minima

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Mumford-Shah Meets Stereo: Integration of Weak Depth Hypotheses

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Unsupervised Neural Rendering for Image Hazing

Li, B., Lin, Y., Bai, J., Hu, P., Lv, J., & Peng, X. (2022) · IEEE Transactions on Image Processing · doi:10.1109/tip.2022.3177321 · Cited by 13

Simulation of Atmospheric Visibility Impairment

Zhang, L., Zhu, A., Zhao, S., & Zhou, Y. (2021) · IEEE Transactions on Image Processing · doi:10.1109/tip.2021.3120044 · Cited by 13

Improved structured light system based on generative adversarial networks for highly-reflective surface measurement

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Optimizing and Auto-tuning Belief Propagation on the GPU

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Enabling stereoscopic high dynamic range video

Selmanović, E., Debattista, K., Bashford‐Rogers, T., & Chalmers, A. G. (2013) · Signal Processing Image Communication · doi:10.1016/j.image.2013.08.004 · Cited by 13

Dense depth estimation using adaptive structured light and cooperative algorithm

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A comparison of key-point descriptors for the stereo matching algorithm

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A new method for inpainting of depth maps from time-of-flight sensors based on a modified closing by reconstruction algorithm

Garduño-Ramón, M. A., Terol‐Villalobos, I. R., Osornio-Rios, R. A., & Morales-Hernandez, L. A. (2017) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2017.05.003 · Cited by 13

CuFusion: Accurate Real-Time Camera Tracking and Volumetric Scene Reconstruction with a Cuboid

Zhang, C., & Hu, Y. (2017) · Italian National Conference on Sensors · doi:10.3390/s17102260 · Cited by 10

A window size selection network for stereo dense image matching

Huang, X., Liu, W., & Qin, R. (2020) · International Journal of Remote Sensing · doi:10.1080/2150704x.2020.1723168 · Cited by 13

Centimeter-wave Free-space Neural Time-of-Flight Imaging

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Boundary-preserving stereo matching with certain region detection and adaptive disparity adjustment

Jung, C., Chen, X., Cai, J., Lei, H., Yun, I., & Kim, J. (2015) · Journal of Visual Communication and Image Representation · doi:10.1016/j.jvcir.2015.08.010 · Cited by 13

Accurate depth estimation using structured light and passive stereo disparity estimation

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Projector Placement Planning for High Quality Visualizations on Real-World Colored Objects

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Recursive Image Dehazing via Perceptually Optimized Generative Adversarial Network (POGAN)

Du, Y., & Li, X. (2019) · IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW) · doi:10.1109/cvprw.2019.00233 · Cited by 13

An ensemble multi-scale residual attention network (EMRA-net) for image Dehazing

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3D reconstruction by polarimetric imaging method based on perspective model

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Robust airlight estimation for haze removal from a single image

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Comparison of Active Structure Lighting Mono and Stereo Camera Systems: Application to 3D Face Acquisition

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Stereo vision architecture for heterogeneous systems-on-chip

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A Novel DIBR 3D Image Watermarking Algorithm Resist to Geometrical Attacks

Wang, S., Cui, C., & Niu, X. (2017) · Chinese Journal of Electronics · doi:10.1049/cje.2017.09.025 · Cited by 13

SymmNet: A Symmetric Convolutional Neural Network for Occlusion DetectionPreprint

Li, A., & Yuan, Z. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1807.00959 · Cited by 13

Using Artificial Intelligence for Assistance Systems to Bring Motor Learning Principles into Real World Motor Tasks

Vandevoorde, K., Vollenkemper, L., Schwan, C., Kohlhase, M., & Schenck, W. (2022) · Sensors · doi:10.3390/s22072481 · Cited by 17

Robust and accurate depth estimation by fusing LiDAR and stereo

Xu, G., Cao, X., Liu, J., Fan, J., Li, E., & Long, X. (2023) · Measurement Science and Technology · doi:10.1088/1361-6501/acef47 · Cited by 12

Depth super-resolution from explicit and implicit high-frequency features

Qiao, X., Ge, C., Zhang, Y., Zhou, Y., Tosi, F., Poggi, M., & Mattoccia, S. (2023) · Computer Vision and Image Understanding · doi:10.1016/j.cviu.2023.103841 · Cited by 12

A digital watermarking for anaglyph 3D images

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Single viewpoint model completion of symmetric objects for digital inspection

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Edge enhancement of depth based rendered images

Farid, M. S., Lucenteforte, M., & Grangetto, M. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7026103 · Cited by 12

A Parameterized Stereo Vision Core for FPGAs

Longfield, S., & Chang, M. L. (2009) · IEEE International Symposium on Field-Programmable Custom Computing Machines (FCCM) · doi:10.1109/fccm.2009.32 · Cited by 12

Vision based object follower automated guided vehicle using compressive tracking and stereo-vision

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Handling Occlusion and Large Displacement Through Improved RGB-D Scene Flow Estimation

Wang, Y., Zhang, J., Liu, Z., Wu, Q., Chou, P. A., Zhang, Z., & Jia, Y. (2015) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2015.2462011 · Cited by 12

Real-Time Subpixel Fast Bilateral Stereo

Fan, R., Liu, Y., Bocus, M. J., Wang, L., & Liu, M. (2018) · IEEE International Conference on Information and Automation (ICIA) · doi:10.1109/icinfa.2018.8812594 · Cited by 12

Enhanced Soft 3D Reconstruction Method with an Iterative Matching Cost Update Using Object Surface Consensus

Lee, M.-J., Um, G., Yun, J., Cheong, W., & Park, S.-Y. (2021) · Sensors · doi:10.3390/s21196680 · Cited by 12

Separable Coded Aperture for Depth from a Single Image

Lin, J., Lin, X., Ji, X., & Dai, Q. (2014) · IEEE Signal Processing Letters · doi:10.1109/lsp.2014.2343251 · Cited by 12

Calibration of double stripe 3D laser scanner systems using planarity and orthogonality constraints

Ozan, Ş., & Gümüştekin, Ş. (2013) · Digital Signal Processing · doi:10.1016/j.dsp.2013.08.006 · Cited by 12

Least squares image matching: A comparison of the performance of robust estimators

Li, Z., & Jinling Wang, A. (2014) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprsannals-ii-1-37-2014 · Cited by 12

Spatial Sampling for Image Segmentation

Rivera, M., Dalmau, O., Mio, W., & Ramírez-Manzanares, A. (2011) · The Computer Journal · doi:10.1093/comjnl/bxr032 · Cited by 12

Shape from interaction

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REVISITING INTRINSIC CURVES FOR EFFICIENT DENSE STEREO MATCHING

Shahbazi, M., Sohn, G., Théau, J., & Ménard, P. (2016) · ISPRS annals of the photogrammetry, remote sensing and spatial information sciences · doi:10.5194/isprs-annals-iii-3-123-2016 · Cited by 12

Performance of RGB-D camera for different object types in greenhouse conditions

Ringdahl, O., Kurtser, P., & Edan, Y. (2019) · European Conference on Mobile Robots · doi:10.1109/ecmr.2019.8870935 · Cited by 12

Image Haze Removal Based on Superpixels and Markov Random Field

Tan, Y., & Wang, G. (2020) · IEEE Access · doi:10.1109/access.2020.2982910 · Cited by 12

Quaternary Census Transform Based on the Human Visual System for Stereo Matching

Ji, S.-W., Kim, S., Lim, D., Jung, S., & Ko, S.-J. (2020) · IEEE Access · doi:10.1109/access.2020.3003919 · Cited by 12

Edge-guided second-order total generalized variation for Gaussian noise removal from depth map

Li, S., Zhang, B., Yang, X., & Zhu, W. (2020) · Scientific Reports · doi:10.1038/s41598-020-73342-3 · Cited by 12

Objective Image Quality Measures for Disparity Maps Evaluation

Merrouche, S., Andrić, M., Bondžulić, B. P., & Bujaković, D. M. (2020) · Electronics · doi:10.3390/electronics9101625 · Cited by 12

Overwater Image Dehazing via Cycle-Consistent Generative Adversarial Network

Zheng, S., Sun, J., Liu, Q., Qi, Y., & Yan, J. (2020) · Electronics · doi:10.3390/electronics9111877 · Cited by 12

Vision and RTLS Safety Implementation in an Experimental Human—Robot Collaboration Scenario

Slovák, J., Melicher, M., Šimovec, M., & Vachálek, J. (2021) · Sensors · doi:10.3390/s21072419 · Cited by 12

Robust depth map inpainting using superpixels and non-local Gauss–Markov random field prior

Satapathy, S., & Sahay, R. R. (2021) · Signal Processing Image Communication · doi:10.1016/j.image.2021.116378 · Cited by 12

The Implicit Values of A Good Hand Shake: Handheld Multi-Frame Neural Depth Refinement

Chugunov, I., Zhang, Y., Xia, Z., Zhang, X., Chen, J., & Heide, F. (2022) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr52688.2022.00287 · Cited by 12

Learning to Produce 3D Media From a Captured 2D Video

Park, M., Luo, J., Gallagher, A., & Rabbani, M. (2013) · IEEE Transactions on Multimedia · doi:10.1109/tmm.2013.2264926 · Cited by 12

A Multi-view Structured-Light System for Highly Accurate 3D Modeling

Ha, H., Oh, T.-H., & Kweon, I. S. (2015) · International Conference on 3D Vision (3DV) · doi:10.1109/3dv.2015.21 · Cited by 12

Inter-Image Statistics for 3D Environment Modeling

Torres-Méndez, L. A., & Dudek, G. (2007) · International Journal of Computer Vision · doi:10.1007/s11263-007-0108-2 · Cited by 12

A MRF Formulation for Coded Structured Light

Tardif, J., & Roy, S. (2005) · International Conference on 3-D Digital Imaging and Modeling (3DIM) · doi:10.1109/3dim.2005.11 · Cited by 12

Effective Very-wide-area 3D ToF Sensing

Paredes, A. L., Conde, M. H., & Loffeld, O. (2021) · Proceedings of IEEE Sensors/Proceedings of IEEE Sensors ... · doi:10.1109/sensors47087.2021.9639585 · Cited by 11

LMHaze: Intensity-aware Image Dehazing with a Large-scale Multi-intensity Real Haze Dataset

Zhang, R., Yang, H., Yang, Y., Fu, Y., & Pan, L. (2024) · ACM International Conference on Multimedia in Asia (MMAsia) · doi:10.1145/3696409.3700178 · Cited by 11

Three-Dimensional Reconstruction Techniques and the Impact of Lighting Conditions on Reconstruction Quality: A Comprehensive Review

Rangelov, D., Waanders, S., Waanders, K., van Keulen, M., & Miltchev, R. I. (2025) · Lights · doi:10.3390/lights1010001 · Cited by 11

smARTbox

Fischbach, M., Latoschik, M. E., Bruder, G., & Steinicke, F. (2012) · Virtual Reality International Conference · doi:10.1145/2331714.2331737 · Cited by 11

Hierarchical example-based range-image super-resolution with edge-preservation

Mandal, S., Bhavsar, A. V., & Sao, A. K. (2014) · Proceedings - International Conference on Image Processing · doi:10.1109/icip.2014.7025785 · Cited by 11

A methodology for remote virtual interaction in teleimmersive environments

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Stereo matching algorithm based on curvelet decomposition and modified support weights

Mukherjee, D., Wang, G., & Wu, Q. M. J. (2010) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp.2010.5495003 · Cited by 11

A local iterative refinement method for adaptive support-weight stereo matching

Psota, E. T., Kowalczuk, J., Carlson, J. D., & Pérez, L. C. (2011) · Cited by 11

Exposure Map Fusion for Precise 3-D Reconstruction of High Dynamic Range Surfaces

Li, J., Guan, J., Chen, X., Le, X., & Xi, J. (2022) · IEEE Transactions on Instrumentation and Measurement · doi:10.1109/tim.2022.3209728 · Cited by 11

Research on the Improvement of Semi-Global Matching Algorithm for Binocular Vision Based on Lunar Surface Environment

Guo, Y., Gu, M., & Xu, Z.-D. (2023) · Sensors · doi:10.3390/s23156901 · Cited by 11

A robust watermarking method for stereo-pair images based on unmatched block bitmap

Ou, Z.-H., & Chen, L. (2015) · Multimedia Tools and Applications · doi:10.1007/s11042-014-2433-0 · Cited by 11

Real-time Stereo-Image Stitching using GPU-based Belief Propagation

Adam, M. G., Jung, C., Roth, S., & Brunnett, G. (2009) · Cited by 11

Partial Enumeration and Curvature Regularization

Olsson, C., Ulén, J., Boykov, Y. Y., & Kolmogorov, V. (2013) · IEEE/CVF International Conference on Computer Vision (ICCV) · doi:10.1109/iccv.2013.365 · Cited by 11

Stereoview to Multiview Conversion Architecture for Auto-Stereoscopic 3D Displays

Kuo, P.-C., Lo, K.-L., Tseng, H.-K., Lee, K., Liu, B.-D., & Yang, J. (2017) · IEEE Transactions on Circuits and Systems for Video Technology · doi:10.1109/tcsvt.2017.2732061 · Cited by 11

An Improved U-Net Architecture for Image Dehazing

Ge, W., Lin, Y., Zhitao, W. A. N. G., Wang, G., & Tan, S. (2021) · IEICE Transactions on Information and Systems · doi:10.1587/transinf.2021edp7043 · Cited by 11

High-Accuracy Stereo Depth Maps . . .

Daniel Scharstein, E. A. (2003)

High-Resolution Stereo Datasets with Subpixel-Accurate Ground Truth

Scharstein, D., Hirschmüller, H., Kitajima, Y., Krathwohl, G., Nešić, N., Wang, X., & Westling, P. (2014) · Lecture notes in computer science · doi:10.1007/978-3-319-11752-2_3 · Cited by 1,377

Stereo HDR Disparity Map Computation Using Structured Light

Akhavan, T., Kapeller, C., Cho, J.-H., & Gelautz, M. (2014) · Cited by 4

HyperDepth: Learning Depth from Structured Light without Matching

Fanello, S., Rhemann, C., Tankovich, V., Kowdle, A., Escolano, S. O., Kim, D., & Izadi, S. (2016) · IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings - IEEE Computer Society Conference on Computer Vision and Pattern Recognition/Proceedings · doi:10.1109/cvpr.2016.587 · Cited by 104

Recovering Stereo Depth Maps Using Single Gaussian Blurred Color Structured Light Pattern

Chen, X., & Yang, Y. (2011) · ERA: Education and Research Archive (University of Alberta) · doi:10.7939/r33b5wf35 · Cited by 1