Indoor Scenes - Groups

SceneNN and ScanNet datasets

SceneNN and ScanNet datasets
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Real-time Progressive 3D Semantic Segmentation for Indoor Scenes

Real-time Progressive 3D Semantic Segmentation for Indoor Scenes
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ARK-itscenes

Diverse real-world dataset for 3D indoor scene understanding using mobile RGB-D data
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3D Semantic Parsing of Large-Scale Indoor Spaces

3D semantic parsing of large-scale indoor spaces.
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MonoSelfRecon

MonoSelfRecon: Purely Self-Supervised Explicit Generalizable 3D Reconstruction of Indoor Scenes from Monocular RGB Views
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Middlebury 2014

The Middlebury 2014 dataset is a benchmark for stereo matching, consisting of 33 pairs of stereo images with sparse depth ground truth.
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Scan2CAD

The Scan2CAD dataset contains object-level human-generated annotations. The annotations include category label, segmentation, a similar CAD model in ShapeNet, and the...
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SUN3D

SUN3D dataset is a large-scale dataset of RGB-D scans of indoor scenes.
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Scannet: Richly-annotated 3D reconstructions of indoor scenes

Scannet: Richly-annotated 3D reconstructions of indoor scenes.
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3DMatch

The 3DMatch [26] is a well-known indoor registration dataset of 62 scenes captured by RGBD sensor.
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ScanNetV2

The dataset used in the paper is ScanNetV2, a real-world dataset for indoor scenes. It contains 1205 training scenes and 312 testing scenes, with instance-level object bounding...
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ScanNet and ArkitScenes

The dataset used in the Point2Pix paper, containing point clouds and camera parameters for indoor scenes.
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Habitat

The Habitat dataset is a large-scale indoor simulator dataset containing 145 semantically-annotated indoor scenes.
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3D-FRONT

3D-FRONT is a synthetic dataset composed of 6,813 houses with 14,629 rooms, where each room is arranged by a collection of high-quality 3D furniture objects from the 3D-FUTURE...
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Scannet

The dataset used for training and testing the proposed RGBD-based obstacle avoidance system for visually impaired people.
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ScanNet Dataset

The ScanNet dataset is a large-scale indoor dataset composed of monocular sequences with ground truth poses and depth images.
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S3DIS

The dataset used in the paper is a real-world 3D point cloud dataset, which is used for 3D shape classification, part segmentation, and shape retrieval tasks.
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17 datasets found