An introduction to depth image datasets

Deep image datasets are a very important data type in deep learning and computer vision tasks. It contains depth information for each pixel and can be used for a variety of applications such as scene reconstruction, object detection, and pose estimation. This article will introduce several commonly used depth image data sets, including their sources, characteristics and applications.

1.NYU Depth V2

NYU Depth V2 dataset contains depth images and RGB images of indoor scenes, with a total of 1449 scene samples. These scenes include various indoor environments such as bedrooms, living rooms, and kitchens. Each scene provides intrinsic and extrinsic parameter information of the camera, which can be used for tasks such as camera pose estimation and scene reconstruction. In addition, the data set also provides annotation information of objects in the scene, which can be used for tasks such as object detection and semantic segmentation.

2.Kinect Fusion

The Kinect Fusion dataset provides RGB-D images of multiple scenes and corresponding 3D models, suitable for Tasks such as scene reconstruction, 3D pose estimation and object detection. In addition, the data set also supports data formats from multiple depth sensors, including devices such as Microsoft Kinect, Asus Xtion Pro Live, and Primesense Carmine 1.08. This data provides researchers and developers with a rich resource for research and development in areas such as deep learning, computer vision, and robotics.

3.SUN RGB-D

SUN RGB-D contains RGB-D images and scene annotation information for indoor and outdoor scenes. The data set contains a total of 10,335 scene samples, of which 5,285 are indoor scenes and 5,050 are outdoor scenes. Each scene provides camera intrinsic and extrinsic parameter information, which can be used for tasks such as camera pose estimation and scene reconstruction. In addition, this data set also provides a variety of scene annotation information, including object categories, semantic segmentation and scene layout, etc., which can be used for tasks such as object detection, semantic segmentation and scene understanding.

4.ScanNet

ScanNet contains RGB-D images and scene annotation information of indoor scenes. The dataset contains a total of 1,513 scene samples, covering a variety of different indoor environments, including offices, shops, schools, etc. Each scene provides camera intrinsic and extrinsic parameter information, which can be used for tasks such as camera pose estimation and scene reconstruction. In addition, this data set also provides a variety of scene annotation information, including object categories, semantic segmentation and scene layout, etc., which can be used for tasks such as object detection, semantic segmentation and scene understanding.

5.3DMatch

3DMatch contains depth images and 3D point cloud data from multiple RGB-D sensors. The dataset contains a total of 1,525 scene samples, covering a variety of different indoor and outdoor environments. Each scene provides camera intrinsic and extrinsic parameter information, which can be used for tasks such as camera pose estimation and scene reconstruction. In addition, this data set also provides rich scene registration information, including point cloud registration and image registration, which can be used for tasks such as 3D reconstruction and scene matching.

In short, depth image datasets are an indispensable data type in the fields of deep learning and computer vision. They can be used for a variety of tasks, such as scene reconstruction, object detection, Pose estimation and semantic segmentation, etc. The data sets introduced above are all commonly used depth image data sets. Their sources are authentic and reliable, and their characteristics and applications are different. Appropriate data sets can be selected for training and evaluation according to the needs of specific tasks.

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