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

Example scripts for the detection of lanes using the ultra fast lane detection model in ONNX.

CO-PILOT: COllaborative Planning and reInforcement Learning On sub-Task curriculum

DARTS-: Robustly Stepping out of Performance Collapse Without Indicators

"Segmenter: Transformer for Semantic Segmentation" reproduced via mmsegmentation

RefineNet: Multi-Path Refinement Networks for High-Resolution Semantic Segmentation

Experiments for Operating Systems Lab (ETCS-352)

Spatiotemporal resampling methods for mlr3

GenGNN: A Generic FPGA Framework for Graph Neural Network Acceleration

A PyTorch implementation of "Graph Wavelet Neural Network" (ICLR 2019)

Code repository accompanying the paper "On Adversarial Robustness: A Neural Architecture Search perspective"

Python wrapper of LSODA (solving ODEs) which can be called from within numba functions.

Boundary-aware Transformers for Skin Lesion Segmentation

This is the code of NeurIPS'21 paper "Towards Enabling Meta-Learning from Target Models".

A PyTorch implementation of Radio Transformer Networks from the paper "An Introduction to Deep Learning for the Physical Layer".

Set of methods to ensemble boxes from different object detection models, including implementation of "Weighted boxes fusion (WBF)" method.

Modified prey-predator system - Modified prey–predator model describes the rate of change for each species by adding coupling terms.

Neural Articulated Radiance Field

source code of Adversarial Feedback Loop Paper

Simple Baselines for Human Pose Estimation and Tracking

Efficient electromagnetic solver based on rigorous coupled-wave analysis for 3D and 2D multi-layered structures with in-plane periodicity