Our project (STPLS3D) aims to provide a large-scale aerial photogrammetry dataset with synthetic and real annotated 3D point clouds for semantic and instance segmentation tasks.
using different ways for data agumentation, etc.), please run prepare_data_statistic_stpls3d.py to get the class_weight, class_radius_mean, and class_numpoint_mean_dict. Change them in hais_run_stpls3d.yaml, hierarchical_aggregation.cpp, and hierarchical_aggregation.cu accordingly. Make ...
Mask3D predicts accurate 3D semantic instances achieving state-of-the-art on ScanNet, ScanNet200, S3DIS and STPLS3D. [Project Webpage] [Paper] [Demo] News 29. October 2023: Check out this easy setup for Mask3D. 17. January 2023: Mask3D is accepted at ICRA 2023. 🔥 14. October 2022...
STPLS3D python -m datasets.preprocessing.stpls3d_preprocessing preprocess \ --data_dir="PATH_TO_STPLS3D" \ --save_dir="data/processed/stpls3d" Training and testing 🚆 Train Mask3D on the ScanNet dataset: python main_instance_segmentation.py ...
we present a richly-annotated synthetic 3D aerial photogrammetry point cloud dataset, termed STPLS3D, with more than 16km2of landscapes and up to 18 fine-grained semantic categories. For verification purposes, we also provide a parallel dataset collected from four areas in the real environment. Ex...