Large-Scale Point Cloud Classification Benchmark.
http://data.ign.fr/benchmarks/UrbanAnalysis/
The database contains 3D MLS data from a dense urban environment in Paris (France), composed of 300 million points.
http://www.cs.cmu.edu/~vmr/datasets/oakland_3d/cvpr09/doc/
This repository contains labeled 3-D point cloud laser data collected from a moving platform in a urban environment.
http://www.cvlibs.net/datasets/kitti/eval_odometry.php
This dataset contains not only point cloud but also image for algorithm evaluate. The odometry benchmark consists of 22 stereo sequences, saved in loss less png format: They provide 11 sequences (00-10) with ground truth trajectories for training and 11 sequences (11-21) without ground truth for evaluation. For this benchmark you may provide results using monocular or stereo visual odometry, laser-based SLAM or algorithms that combine visual and LIDAR information. This dataset contains a development kit providing details about the data format.
This dataset is based on original KITTI dataset and includes instance annotation for all traffic participants (static and moving). This dataset is built for explore the semantic information in point cloud for SLAM algorithms and etc.
http://paopaorobot.org/bbs/read.php?tid=155&fid=18
From now on, PaoPaoRobot, an Official Accounts, has already collected many public dataset download links for academicly using, including the following datasets:
TUM： https://pan.baidu.com/s/1UPtit23QOLl5tBD56TKsjA
KITTI： https://pan.baidu.com/s/1E9wv2Erb-O2v_wAxBdAqQw
DSO： https://pan.baidu.com/s/1BqHvvPWxbRIs0NMY0Yhzrg
Mono： https://pan.baidu.com/s/1HMnNegLaw6orHSKb_bJfIA
EuRoC： https://pan.baidu.com/s/1GJL5QK9Vjd9hOdhGXyTOkA