Inside Airbnb is an independent, non-commercial set of tools and data that allows you to explore how Airbnb is REALLY being used in cities around the world.
The following data directories include examples and a little information about various file formats e.g.
- off, ply, obj, pcd, 3ds, etc.
- mp4, mov, mpg, etc.
- bmp, png, tif, etc.
Announcing the SUMO challenge - a contest to encourage the development of algorithms for complete understanding of 3D indoor scenes from 360° RGB-D panoramas with the goal of enabling social AR and VR research and experiences.
Gibson’s underlying database of spaces includes 572 full buildings composed of 1447 floors covering a total area of 211k m2s. The database is collected from real indoor spaces using 3D scanning and reconstruction. For each space, we provide: the 3D reconstruction, RGB images, depth, surface normal, and for a fraction of the spaces, semantic object annotations. In this page you can see various visualizations for each space, including 3D dissections, exploration using a randomly controlled husky agent, and standard point-to-point navigation episodes
VGG Image Annotator (VIA) is an image annotation tool that can be used to define regions in an image and create textual descriptions of those regions. VIA is an open source project developed at the Visual Geometry Group and released under the BSD-2 clause license.
Making 27.91TB of research data available!
We've designed a distributed system for sharing enormous datasets - for researchers, by researchers. The result is a scalable, secure, and fault-tolerant repository for data, with blazing fast download speeds. Contact us at contact@academictorrents.com.
Repository to track the progress in Natural Language Processing (NLP), including the datasets and the current state-of-the-art for the most common NLP tasks.
Marvin is a deep learning framework designed first and foremost to be hackable. It is naively simple for fast prototyping, uses only basic C/C++, and only calls CUDA and cuDNN as dependencies.
A. Dulny, A. Hotho, and A. Krause. Machine Learning and Knowledge Discovery in Databases: Research Track, page 438--455. Cham, Springer Nature Switzerland, (2023)
A. Dulny, A. Hotho, and A. Krause. Machine Learning and Knowledge Discovery in Databases: Research Track, page 438--455. Cham, Springer Nature Switzerland, (2023)
A. Dulny, A. Hotho, and A. Krause. Machine Learning and Knowledge Discovery in Databases: Research Track, page 438--455. Cham, Springer Nature Switzerland, (2023)