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3D-Machine-Learning - A resource repository for 3D machine learning

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In recent years, tremendous amount of progress is being made in the field of 3D Machine Learning, which is an interdisciplinary field that fuses computer vision, computer graphics and machine learning. This repo is derived from my study notes and will be used as a place for triaging new research papers. To contribute to this Repo, you may add content through pull requests or open an issue to let me know.

Objectron - Objectron is a dataset of short, object-centric video clips

  •    Jupyter

Objectron is a dataset of short object centric video clips with pose annotations. The Objectron dataset is a collection of short, object-centric video clips, which are accompanied by AR session metadata that includes camera poses, sparse point-clouds and characterization of the planar surfaces in the surrounding environment. In each video, the camera moves around the object, capturing it from different angles. The data also contain manually annotated 3D bounding boxes for each object, which describe the object’s position, orientation, and dimensions. The dataset consists of 15K annotated video clips supplemented with over 4M annotated images in the following categories: bikes, books, bottles, cameras, cereal boxes, chairs, cups, laptops, and shoes. In addition, to ensure geo-diversity, our dataset is collected from 10 countries across five continents. Along with the dataset, we are also sharing a 3D object detection solution for four categories of objects — shoes, chairs, mugs, and cameras. These models are trained using this dataset, and are released in MediaPipe, Google's open source framework for cross-platform customizable ML solutions for live and streaming media.

Kimera - Index repo for Kimera code

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Kimera is a C++ library for real-time metric-semantic simultaneous localization and mapping, which uses camera images and inertial data to build a semantically annotated 3D mesh of the environment. Kimera is modular, ROS-enabled, and runs on a CPU. Kimera is partially funded by ARL DCIST, ONR RAIDER, MIT Lincoln Laboratory, and “la Caixa” Foundation (ID 100010434), LCF/BQ/AA18/11680088 (A. Rosinol).




TEASER-plusplus - A fast and robust point cloud registration library

  •    C++

TEASER++ is a fast and certifiably-robust point cloud registration library written in C++, with Python and MATLAB bindings. Left: correspondences generated by 3DSmoothNet (green and red lines represent the inlier and outlier correspondences according to the ground truth respectively). Right: alignment estimated by TEASER++ (green dots represent inliers found by TEASER++).

gradslam - gradslam is an open source differentiable dense SLAM library for PyTorch

  •    Python

gradslam is a fully differentiable dense SLAM framework. It provides a repository of differentiable building blocks for a dense SLAM system, such as differentiable nonlinear least squares solvers, differentiable ICP (iterative closest point) techniques, differentiable raycasting modules, and differentiable mapping/fusion blocks. One can use these blocks to construct SLAM systems that allow gradients to flow all the way from the outputs of the system (map, trajectory) to the inputs (raw color/depth images, parameters, calibration, etc.). You should see the version number displayed.

OpenSfM - Open source Structure-from-Motion pipeline

  •    Python

OpenSfM is a Structure from Motion library written in Python. The library serves as a processing pipeline for reconstructing camera poses and 3D scenes from multiple images. It consists of basic modules for Structure from Motion (feature detection/matching, minimal solvers) with a focus on building a robust and scalable reconstruction pipeline. It also integrates external sensor (e.g. GPS, accelerometer) measurements for geographical alignment and robustness. A JavaScript viewer is provided to preview the models and debug the pipeline.

BundleFusion - [Siggraph 2017] BundleFusion: Real-time Globally Consistent 3D Reconstruction using Online Surface Re-integration

  •    C++

You are free to use this code with proper attribution in non-commercial applications (Please see License.txt). More information about this project can be found in our paper and project website.


AliceVision - Photogrammetric Computer Vision Framework

  •    C++

AliceVision is a Photogrammetric Computer Vision Framework which provides a 3D Reconstruction and Camera Tracking algorithms. AliceVision aims to provide strong software basis with state-of-the-art computer vision algorithms that can be tested, analyzed and reused. The project is a result of collaboration between academia and industry to provide cutting-edge algorithms with the robustness and the quality required for production usage. Learn more details about the pipeline and tools based on it on AliceVision website.

differentiable_volumetric_rendering - This repository contains the code for the CVPR 2020 paper "Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision"

  •    Python

This repository contains the code for the paper Differentiable Volumetric Rendering: Learning Implicit 3D Representations without 3D Supervision. You can find detailed usage instructions for training your own models and using pre-trained models below.

semantic_slam - Real time semantic slam in ROS with a hand held RGB-D camera

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Semantic SLAM can generate a 3D voxel based semantic map using only a hand held RGB-D camera (e.g. Asus xtion) in real time. We use ORB_SLAM2 as SLAM backend, a CNN (PSPNet) to produce semantic prediction and fuse semantic information into a octomap. Note that our system can also be configured to generate rgb octomap without semantic information. This project is released under a GPLv3 license.

intrinsic3d - Intrinsic3D - High-Quality 3D Reconstruction by Joint Appearance and Geometry Optimization with Spatially-Varying Lighting (ICCV 2017)

  •    C++

Copyright (c) 2019, NVIDIA Corporation and Technical University of Munich. All Rights Reserved. The Intrinsic3D source code is available under the BSD license, please see the LICENSE file for details. All data in the Intrinsic3D Dataset is licensed under a Creative Commons 4.0 Attribution License (CC BY 4.0), unless stated otherwise. Intrinsic3D is a method to obtain high-quality 3D reconstructions from low-cost RGB-D sensors. The algorithm recovers fine-scale geometric details and sharp surface textures by simultaneously optimizing for reconstructed geometry, surface albedos, camera poses and scene lighting.

Matterport - Matterport3D is a pretty awesome dataset for RGB-D machine learning tasks :)

  •    C++

The Matterport3D V1.0 dataset contains data captured throughout 90 properties with a Matterport Pro Camera. This repository includes the raw data for the dataset plus derived data, annotated data, and scripts/models for several scene understanding tasks.

convolutional_occupancy_networks - [ECCV'20] Convolutional Occupancy Networks

  •    Python

Contact Songyou Peng for questions, comments and reporting bugs. First you have to make sure that you have all dependencies in place. The simplest way to do so, is to use anaconda.

tsdf-fusion - Fuse multiple depth frames into a TSDF voxel volume.

  •    Cuda

CUDA/C++ code to fuse multiple registered depth maps into a projective truncated signed distance function (TSDF) voxel volume, which can then be used to create high quality 3D surface meshes and point clouds. Tested on Ubuntu 14.04 and 16.04. Looking for an older version? See here.

MeshroomMaya - Photomodeling plugin for Maya

  •    C++

MeshroomMaya is a Maya plugin that enables to model 3D objects from images. MeshroomMaya is licensed under MPL v2 license.






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