江哥's repositories
semantic-segmentation-editor
Web labeling tool for bitmap images and point clouds
kaolin
A PyTorch Library for Accelerating 3D Deep Learning Research
covid-chestxray-dataset
We are building an open database of COVID-19 cases with chest X-ray or CT images.
deeppointcloud-benchmarks
Pytorch framework for doing deep learning on point clouds.
RandLA-Net
🔥RandLA-Net in Tensorflow (CVPR 2020, Oral)
deepin-wine-ubuntu
Deepin Wine for Ubuntu/Debian
3D-Machine-Learning
A resource repository for 3D machine learning
1527398464-qq.com
点云深度学习资料
GitHub-Chinese-Top-Charts
:cn: GitHub中文排行榜，帮助你发现高分优秀中文项目、更高效地吸收国人的优秀经验成果；榜单每周更新一次，敬请关注！（武汉加油！**加油！世界加油！）
CVPR2020-Paper-Code-Interpretation
cvpr2020/cvpr2019／cvpr2018/cvpr2017 papers，极市团队整理
awesome-point-cloud-analysis
A list of papers and datasets about point cloud analysis (processing)
PointCloudSegmentation
Experiments on point cloud segmentation.
Open3D-PointNet2-Semantic3D
Semantic3D segmentation with Open3D and PointNet++
Pseudo_Lidar_V2
(ICLR) Pseudo-LiDAR++: Accurate Depth for 3D Object Detection in Autonomous Driving
kitti_object_vis
KITTI Object Visualization (Birdview, Volumetric LiDar point cloud )
DIoU
Distance-IoU Loss: Faster and Better Learning for Bounding Box Regression (AAAI 2020)
superpoint_graph
Large-scale Point Cloud Semantic Segmentation with Superpoint Graphs
depth_clustering
:taxi: Fast and robust clustering of point clouds generated with a Velodyne sensor.
PointCloudSVMDemo
三维点云激光分类（建筑，树木）
second.pytorch
SECOND for KITTI/NuScenes object detection
PROJECT1
Context This dataset contains tree observations from four areas of the Roosevelt National Forest in Colorado. All observations are cartographic variables (no remote sensing) from 30 meter x 30 meter sections of forest. There are over half a million measurements total! Content This dataset includes information on tree type, shadow coverage, distance to nearby landmarks (roads etcetera), soil type, and local topography. Inspiration Can you build a model that predicts what types of trees grow in an area based on the surrounding characteristics? A past Kaggle competition project on this topic can be found here. What kinds of trees are most common in the Roosevelt National Forest? Which tree types can grow in more diverse environments? Are there certain tree types that are sensitive to an environmental factor, such as elevation or soil type? Data Set Information: Predicting forest cover type from cartographic variables only (no remotely sensed data). The actual forest cover type for a given observation (30 x 30 meter cell) was determined from US Forest Service (USFS) Region 2 Resource Information System (RIS) data. Independent variables were derived from data originally obtained from US Geological Survey (USGS) and USFS data. Data is in raw form (not scaled) and contains binary (0 or 1) columns of data for qualitative independent variables (wilderness areas and soil types). This study area includes four wilderness areas located in the Roosevelt National Forest of northern Colorado. These areas represent forests with minimal human-caused disturbances, so that existing forest cover types are more a result of ecological processes rather than forest management practices. Some background information for these four wilderness areas: Neota (area 2) probably has the highest mean elevational value of the 4 wilderness areas. Rawah (area 1) and Comanche Peak (area 3) would have a lower mean elevational value, while Cache la Poudre (area 4) would have the lowest mean elevational value. As for primary major tree species in these areas, Neota would have spruce/fir (type 1), while Rawah and Comanche Peak would probably have lodgepole pine (type 2) as their primary species, followed by spruce/fir and aspen (type 5). Cache la Poudre would tend to have Ponderosa pine (type 3), Douglas-fir (type 6), and cottonwood/willow (type 4). The Rawah and Comanche Peak areas would tend to be more typical of the overall dataset than either the Neota or Cache la Poudre, due to their assortment of tree species and range of predictive variable values (elevation, etc.) Cache la Poudre would probably be more unique than the others, due to its relatively low elevation range and species composition. Data_Dictionary Elevation = Elevation in meters. Aspect = Aspect in degrees azimuth. Slope = Slope in degrees. Horizontal_Distance_To_Hydrology = Horizontal distance to nearest surface water features. Vertical_Distance_To_Hydrology = Vertical distance to nearest surface water features. Horizontal_Distance_To_Roadways = Horizontal distance to nearest roadway. Hillshade_9am = Hill shade index at 9am, summer solstice. Value out of 255. Hillshade_Noon = Hill shade index at noon, summer solstice. Value out of 255. Hillshade_3pm = Hill shade index at 3pm, summer solstice. Value out of 255. Horizontal_Distance_To_Fire_Point = sHorizontal distance to nearest wildfire ignition points. Wilderness_Area1 = Rawah Wilderness Area Wilderness_Area2 = Neota Wilderness Area Wilderness_Area3 = Comanche Peak Wilderness Area Wilderness_Area4 = Cache la Poudre Wilderness Area Soil_Type1 to Soil_Type40 [Total 40 Types] Cover_TypeForest Cover Type designation. Integer value between 1 and 7, with the following key: Spruce/Fir Lodgepole Pine Ponderosa Pine Cottonwood/Willow Aspen Douglas-fir Krummholz