This repository considers papers that optimize robot and object states based on Monocular, RGB-D, and LiDAR, as well as papers that perform loop closure detection through semantic scene matching.
🔥This repository is still under construction and will continue to be updated!🔥
24.08.01
First commit!
[1]: JIRS = Journal of Intelligent & Robotic Systems
[2]: JVCIR = Journal of Visual Communication and Image Representation
Dataset Name | Description | Link |
---|---|---|
KITTI | A well-known dataset for autonomous driving research, offering stereo and LiDAR data for tasks like object detection, tracking, and SLAM. | KITTI |
SemanticKITTI | A large-scale dataset providing semantic segmentation labels for LiDAR point clouds, based on the KITTI Odometry Benchmark, suitable for autonomous driving applications. | SemanticKITTI |
Replica | A highly detailed and realistic dataset of 18 indoor environments, providing photorealistic textures and semantic annotations for 3D research. | Replica |
TUM | A dataset for evaluating RGB-D SLAM systems, containing benchmark sequences recorded with a Microsoft Kinect camera. | TUM |
Matterport3D | A large dataset of indoor RGB-D images with 3D meshes and semantic annotations, used for developing scene understanding algorithms. | Matterport3D |
SUN RGB-D | A dataset containing RGB-D images from various sensors, annotated with 3D bounding boxes for object detection and scene understanding research. | SUN RGB-D |
Microsoft-RGBD | A dataset featuring RGB-D sequences captured in various indoor environments, used for scene understanding and 3D reconstruction tasks. | Microsoft-RGBD |
NYU Depth v2 | An RGB-D dataset collected with a Kinect sensor, providing labeled depth maps and semantic annotations for indoor scenes. | NYU Depth v2 |
Redwood-OS | A collection of RGB-D sequences designed for evaluating 3D reconstruction algorithms, with a focus on object and scene scanning. | Redwood-OS |
RGBD Scenes v1 | A dataset with RGB-D video sequences for studying 3D object recognition and scene understanding, featuring cluttered indoor environments. | RGBD Scenes v1 |
RGBD Scenes v2 | An extended version of RGBD Scenes v1, providing additional objects and sequences for more complex scene analysis. | RGBD Scenes v2 |
ICL-NUIM | A synthetic RGB-D dataset for benchmarking SLAM and visual odometry algorithms, featuring photo-realistic scenes. | ICL-NUIM |
ScanNet | A richly annotated dataset with RGB-D video sequences of indoor environments, aimed at advancing 3D scene understanding and reconstruction. | ScanNet |
YCB-Video | A dataset for benchmarking 6-DoF object pose estimation in video, including real and synthetic sequences of household objects. | YCB-Video |
ApolloScape | A comprehensive dataset for autonomous driving, providing annotated 2D/3D images and point clouds from real-world traffic scenarios. | ApolloScape |
Cityscapes | A large-scale dataset for semantic urban scene understanding, featuring pixel-level annotations for various urban street scenes. | Cityscapes |