Resources - AgEagle Aerial Systems Inc.
Newsletter sign up Sign up to receive updates directly to your inbox. AgEagle Aerial Systems Inc. is proudly listed on the NYSE American under the trading symbol UAVS
ShapeNet
ShapeNet is an ongoing effort to establish a richly-annotated, large-scale dataset of 3D shapes. We provide researchers around the world with this data to enable research in computer graphics, computer vision, robotics, and other related disciplines. ShapeNet is a collaborative effort between researchers at Princeton, Stanford and TTIC. ShapeNetCore ShapeNetCore is a subset of the full ShapeNet da
A Large Dataset of Object Scans
Dataset download Please refer to https://github.com/intel-isl/redwood-3dscan on how to download the dataset. Abstract We have created a dataset of more than ten thousand 3D scans of real objects. To create the dataset, we recruited 70 operators, equipped them with consumer-grade mobile 3D scanning setups, and paid them to scan objects in their environments. The operators scanned objects of their c
koptinspection:koptinspection – ASL Datasets
You may find all the datasets following this Link. All datasets contain dense point clouds derived using the proposed autonomous inspection planner for the flight path, a camera system and the Pix4D software for post-processing of the pose-annotated images. The point clouds are saved in *.ply format and you may load them with any relevant software (e.g. MeshLab). Furthermore, for the case of the M
Media - Point Cloud Library (PCL)
The following sections show a number of media entries for the PCL project, ranging from a visual history of the project to a list of research presentations given by various PCL developers. Please click on the title of each section to expand it. PCL became a first-class citizen project in March 2011, when Radu B. Rusu, while working as a Research Scientist at Willow Garage, decided to create a sepa
Princeton Shape Benchmark
The Princeton Shape Benchmark provides a repository of 3D models and software tools for evaluating shape-based retrieval and analysis algorithms. The motivation is to promote the use of standardized data sets and evaluation methods for research in matching, classification, clustering, and recognition of 3D models. Researchers are encouraged to use these resources to produce comparisons of compet
Robotic 3D Scan Repository
This repository provides: 3D point clouds from robotic experiments log files of robot runs standard 3D data sets for the robotics community You may freely use this data for developing SLAM or interpretation algorithms, but you are required to name the people, who recorded the data set and their correponding institution. If you want to contribute a data set please contact: The following data sets a
NYU Depth V2 « Nathan Silberman
If you use the dataset, please cite the following work: Indoor Segmentation and Support Inference from RGBD Images ECCV 2012 [PDF][Bib] Samples of the RGB image, the raw depth image, and the class labels from the dataset. Overview The NYU-Depth V2 data set is comprised of video sequences from a variety of indoor scenes as recorded by both the RGB and Depth cameras from the Microsoft Kinect. It fea
リリース、障害情報などのサービスのお知らせ
最新の人気エントリーの配信
j次のブックマーク
k前のブックマーク
lあとで読む
eコメント一覧を開く
oページを開く
Thingi10K
Robust Reconstruction of Indoor Scenes
The Shape COSEG Dataset
Princeton ModelNet
Robotics Institute: Vision and Mobile Robotics Laboratory
SLIDAR - MOUT
related_links – ASL Datasets
RGB-D (Kinect) Object Dataset
Research Datasets
3D Scene Dataset from the paper "Dense Scene Reconstruction with Points of Interest"
ECCV 2014: Tutorial on 3D Scene Understanding
vision.ucsd.edu | Where vision is our vision