A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor

Last update: Dec 19, 2022

Related tags

Deep Learning Phase-SLAM

Overview

Phase-SLAM

A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor

This open source is written by MATLAB

Run Mode

Open one of the Name_00xU.m or Name_00xR.m by MATLAB, for example David_001U.m (each of them corresponds to a dataset).
Run it.
No other dependencies are required.

The ground-truth models and reconstruction results are shown in fig:

The (a)-(c) are unreal datasets built in Unreal Engineer 4 simulator, (e)-(f) are real-world datasets collected by structured light sensor with UR5 robotic arm. The bottom row is ground truth, the top row is result.

Related Paper

Phase-SLAM: Mobile Structured Light Illumination for Full Body 3d Scanning, Xi Zheng, Rui Ma, Rui Gao, and Qi Hao, IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS). IEEE, 2021
Phase-SLAM: Phase Based Simultaneous Localization and Mapping for Mobile Structured Light Illumination Systems, Xi Zheng, Rui Ma, Rui Gao, and Qi Hao, under review

Owner

Xi Zheng

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A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor

Related tags

Overview

Phase-SLAM

A Pose Estimator for Dense Reconstruction with the Structured Light Illumination Sensor

Run Mode

Related Paper

Owner

Xi Zheng

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Bayesian inference for Permuton-induced Chinese Restaurant Process (NeurIPS2021).

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Papers about explainability of GNNs

PyTorch implementation of our paper How robust are discriminatively trained zero-shot learning models?

Unicorn can be used for performance analyses of highly configurable systems with causal reasoning