computer-vision

Mixed Reality

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in mixed-reality

Trend

Dataset

Best Model

Actions

No benchmarks available.

Libraries

i

Use these libraries to find mixed-reality models and implementations

Datasets

DREAMING Inpainting Dataset

Subtasks

No subtasks available.

Most implemented papers

Normalized Object Coordinate Space for Category-Level 6D Object Pose and Size Estimation

L. Guibas, Shuran Song, Srinath Sridhar, He Wang, Jingwei Huang, Julien P. C. Valentin•Tue Jan 08 2019

The proposed method is able to robustly estimate the pose and size of unseen object instances in real environments while also achieving state-of-the-art performance on standard 6D pose estimation benchmarks.

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Paper Graph

Artificial Intelligence Assisted Infrastructure Assessment using Mixed Reality Systems

F. Catbas, Enes Karaaslan, Ulas Bagci•Sat Dec 08 2018

This study explains in detail the described system and related methodologies of implementing attention guided semisupervised deep learning into MR technology, which interacts with the human inspector during assessment, and how the inspector and the AI will collaborate/communicate for improved visual inspection.

69 0

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HoloLens 2 Research Mode as a Tool for Computer Vision Research

Dorin Ungureanu, Federica Bogo, Silvano Galliani, Pooja Sama, Xin Duan, Casey Meekhof, Jan Stühmer, T. Cashman, Bugra Tekin, Johannes L. Schönberger, Pawel Olszta, M. Pollefeys•Mon Aug 24 2020

HoloLens 2 Research Mode is presented, an API and a set of tools enabling access to the raw sensor streams are provided and it is shown how to combine the Research Mode sensor data with the built-in eye and hand tracking capabilities provided by HoloLens 2.

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Neural RGB-D Surface Reconstruction

M. Nießner, Ricardo Martin-Brualla, Dan B. Goldman, Justus Thies, Dejan Azinovi'c•Thu Apr 08 2021

This work proposes to represent the surface using an implicit function (truncated signed distance function), and shows how to incorporate this representation in the NeRF framework, and extend it to use depth measurements from a commodity RGB-D sensor, such as a Kinect.

348 0

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Virtual, Augmented, and Mixed Reality for Human-robot Interaction: A Survey and Virtual Design Element Taxonomy

Tathagata Chakraborti, M. Walker, Thao Phung, T. Williams, D. Szafir•Tue Feb 22 2022

A novel taxonomic framework for different types of VAM-HRI interfaces is presented, composed of four main categories of virtual design elements (VDEs) and explained how its elements have been developed over the past 30 years as well as the current directions VAM-HRI is headed in the coming decade.

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Efficient Online Surface Correction for Real-time Large-Scale 3D Reconstruction

D. Cremers, Robert Maier, Raphael Schaller•Thu Aug 31 2017

This work proposes an efficient on-the-fly surface correction method for globally consistent dense 3D reconstruction of large-scale scenes that requires only a single GPU and allows for real-time surface correction of large environments.

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Learning Convolutional Transforms for Lossy Point Cloud Geometry Compression

Maurice Quach, G. Valenzise, F. Dufaux•Tue Mar 19 2019

This paper presents a novel data-driven geometry compression method for static point clouds based on learned convolutional transforms and uniform quantization that outperforms the MPEG reference solution in terms of rate-distortion on the Microsoft Voxelized Upper Bodies dataset.

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Context-Aware Mixed Reality: A Framework for Ubiquitous Interaction

Long Chen, Wen Tang, N. John, T. Wan, J. Zhang•Tue Mar 13 2018

A semantic based interactive MR framework is presented that exceeds the current geometry level approaches, a step change in generating high-level context-aware interactions and generates semantic properties of the real world environment through dense scene reconstruction and deep image understanding.

18 0

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Improved Deep Point Cloud Geometry Compression

Maurice Quach, G. Valenzise, F. Dufaux•Mon Jun 15 2020

A set of contributions to improve deep point cloud compression, i.e.: using a scale hyperprior model for entropy coding; employing deeper transforms; a different balancing weight in the focal loss; optimal thresholding for decoding; and sequential model training are proposed.

136 0

Paper Graph

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