Scannet.dk - ScanNet | Richly-annotated 3D Reconstructions of Indoor Scenes

Provided by Alexa ranking, scannet.dk has ranked N/A in N/A and 4,525,896 on the world. scannet.dk reaches roughly 684 users per day and delivers about 20,512 users each month. The domain scannet.dk uses a Commercial suffix and it's server(s) are located in N/A with the IP number 46.36.210.15 and it is a .dk domain.

WebFeb 1, 2017 · ScanNet is an RGB-D video dataset containing 2.5 million views in more than 1500 scans, annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations.

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ScanNet | Richly-annotated 3D Reconstructions of Indoor Scenes
scan-net.org

http://www.scan-net.org/

WebFeb 1, 2017 · ScanNet is an RGB-D video dataset containing 2.5 million views in more than 1500 scans, annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations.

DA: 36 PA: 75 MOZ Rank: 32

GitHub - ScanNet/ScanNet
github.com

https://github.com/ScanNet/ScanNet

WebScanNet is an RGB-D video dataset containing 2.5 million views in more than 1500 scans, annotated with 3D camera poses, surface reconstructions, and instance-level semantic segmentations. ScanNet Data

DA: 16 PA: 10 MOZ Rank: 68

ScanNet Indoor Scene Understanding Challenge
scan-net.org

http://www.scan-net.org/cvpr2021workshop/

WebThe ScanNet dataset is a large-scale semantically annotated dataset of 3D mesh reconstructions of interior spaces (approx. 1500 rooms and 2.5 million RGB-D frames). It is used by more than 480 research groups to develop and benchmark state-of-the-art approaches in semantic scene understanding.

DA: 59 PA: 51 MOZ Rank: 50

ScanNet Dataset | Papers With Code
paperswithcode.com

https://paperswithcode.com/dataset/scannet

WebScanNet is an instance-level indoor RGB-D dataset that includes both 2D and 3D data. It is a collection of labeled voxels rather than points or objects. Up to now, ScanNet v2, the newest version of ScanNet, has collected 1513 annotated scans with an approximate 90% surface coverage.

DA: 21 PA: 39 MOZ Rank: 8

ScanNet++ Dataset
in.tum.de

https://kaldir.vc.in.tum.de/scannetpp/

WebOct 12, 2023 · ScanNet++ is a large-scale, high-fidelity dataset of 3D indoor scenes containing sub-millimeter resolution laser scans, registered 33-megapixel DSLR images, and commodity RGB-D streams from iPhone. The 3D reconstructions are annotated with long-tail and label-ambiguous semantics to benchmark semantic understanding methods, …

DA: 17 PA: 94 MOZ Rank: 93

ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
arxiv.org

https://arxiv.org/abs/2308.11417

WebAug 22, 2023 · We present ScanNet++, a large-scale dataset that couples together capture of high-quality and commodity-level geometry and color of indoor scenes. Each scene is captured with a high-end laser scanner at sub-millimeter resolution, along with registered 33-megapixel images from a DSLR camera, and RGB-D streams from an iPhone. Scene …

DA: 67 PA: 17 MOZ Rank: 91

ScanNet: Richly-Annotated 3D Reconstructions of Indoor …
thecvf.com

https://openaccess.thecvf.com/content_cvpr_2017/papers/Dai_ScanNet_Richly-Annotated_3D_CVPR_2017_paper.pdf

WebIn this paper, we introduce ScanNet, a dataset of richly-annotated RGB-D scans of real-world environments con-taining 2.5M RGB-D images in 1513 scans acquired in 707 distinct spaces. The sheer magnitude of this dataset is larger than any other [58, 81, 92, 75, 3, …

DA: 98 PA: 48 MOZ Rank: 98

ScanNet: Richly-Annotated 3D Reconstructions of Indoor Scenes
ieee.org

https://ieeexplore.ieee.org/document/8099744/

WebTo address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations.

DA: 46 PA: 42 MOZ Rank: 37

ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
arxiv.org

https://arxiv.org/abs/1702.04405

WebFeb 14, 2017 · To address this issue, we introduce ScanNet, an RGB-D video dataset containing 2.5M views in 1513 scenes annotated with 3D camera poses, surface reconstructions, and semantic segmentations. To collect this data, we designed an easy-to-use and scalable RGB-D capture system that includes automated surface reconstruction …

DA: 20 PA: 40 MOZ Rank: 57

ScanNet: Richly-annotated 3D Reconstructions of Indoor Scenes
princeton.edu

https://gfx.cs.princeton.edu/pubs/Dai_2017_SR3/index.php

WebWe show that using this data helps achieve state-of-the-art performance on several 3D scene understanding tasks, including 3D object classification, semantic voxel labeling, and CAD model retrieval. The dataset is freely available at http://www.scan-net.org.

DA: 7 PA: 77 MOZ Rank: 60

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