Displaying 1 to 9 from 9 results

Open3D - Open3D: A Modern Library for 3D Data Processing

  •    C++

Open3D is an open-source library that supports rapid development of software that deals with 3D data. The Open3D frontend exposes a set of carefully selected data structures and algorithms in both C++ and Python. The backend is highly optimized and is set up for parallelization. We welcome contributions from the open-source community. Please cite our work if you use Open3D.

RandLA-Net - 🔥RandLA-Net in Tensorflow (CVPR 2020, Oral & IEEE TPAMI 2021)

  •    Python

This code has been tested with Python 3.5, Tensorflow 1.11, CUDA 9.0 and cuDNN 7.4.1 on Ubuntu 16.04. Update 03/21/2020, pre-trained models and results are available now. You can download the pre-trained models and results here. Note that, please specify the model path in the main function (e.g., main_S3DIS.py) if you want to use the pre-trained model and have a quick try of our RandLA-Net.

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.

3d-pose-baseline - A simple baseline for 3d human pose estimation in tensorflow

  •    Python

Julieta Martinez, Rayat Hossain, Javier Romero, James J. Little. A simple yet effective baseline for 3d human pose estimation. In ICCV, 2017. https://arxiv.org/pdf/1705.03098.pdf. The code in this repository was mostly written by Julieta Martinez, Rayat Hossain and Javier Romero.




layered-scene-inference - Code accompanying the paper "Layer-structured 3D Scene Inference via View Synthesis", ECCV 2018

  •    Python

Layer-structured 3D Scene Inference via View Synthesis Shubham Tulsiani, Richard Tucker, Noah Snavely In ECCV, 2018. Please note that this is not an officially supported Google product.

learningrigidity - Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation (ECCV 2018)

  •    Python

Copyright (c) 2018 NVIDIA Corp. All Rights Reserved. This work is licensed under the Creative Commons Attribution NonCommercial ShareAlike 4.0 License. This repository includes the implementation of our full inference algorithm, including rigidity network, flow and the refinement optimization.

label-fusion - Volumetric Fusion of Multiple Semantic Labels and Masks

  •    C++

C++ code to fuse multiple object labels or mask into OctoMap, which can be then used for 3d reconstruction of objects. It works with and without depth inputs, so can be applied for depth insensible objects: texture-less (for stereo), black (for ir), and transparent. MIT License (see LICENSE file).


model-zoo - Implementations of various Deep Learning models in PyTorch and TensorFlow.

  •    Python

This repository contains implementations of various deep learning research papers. The models are broadly categorised into the folders Generative (e.g. various generative models), NLP (e.g. various recurrent neural networks (RNNs) and natural language processing (NLP) models), Classification (e.g. various CNN models to classify images), Object Detection, Multimodal , Super resolution , 3D Computer Vision. See the READMEs of respective models for more information.






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