An efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc
This is the official implementation of our BEV-Seg3D-Net, an efficient 3D semantic segmentation framework for Urban-scale point clouds like SensatUrban, Campus3D, etc.
Features of our framework/model:
- leveraging various proven methods in 2D segmentation for 3D tasks
- achieve competitive performance in the SensatUrban benchmark
- fast inference process, about 1km^2 area per minute with RTX 3090.
To be done:
- add more complex/efficient fusion models
- add more backbone like ResNeXt, HRNet, DenseNet, etc.
- add more novel projection methods like pointpillars
For technical details, please refer to:
Efficient Urban-scale Point Clouds Segmentation with BEV Projection
Zhenhong Zou, Yizhe Li, Xinyu Zhang
(1) Setup
This code has been tested with Python