3D Semantic Segmentation for Large-Scale Scene Understanding
Kiran Akadas and Shankar Gangisetty
KLE Technological University, Hubballi, India
[email protected], [email protected]
年份：2020
期刊/會議：ACCV
代碼：https://github.com/KiranAkadas/GRanDNet
1、創新
- 基于RandLA-net，随機采樣，速度快、效率高
- 使用了空洞卷積
- GeLU作為激活函數，對拟合方程更有幫助
- 提出了可選的CRF優化方法
2、具體實作
2020-3D Semantic Segmentation for Large-Scale Scene3D Semantic Segmentation for Large-Scale Scene Understanding 3、實驗結果
SHREC 2020
2020-3D Semantic Segmentation for Large-Scale Scene3D Semantic Segmentation for Large-Scale Scene Understanding 2020-3D Semantic Segmentation for Large-Scale Scene3D Semantic Segmentation for Large-Scale Scene Understanding 2020-3D Semantic Segmentation for Large-Scale Scene3D Semantic Segmentation for Large-Scale Scene Understanding S3DIS
2020-3D Semantic Segmentation for Large-Scale Scene3D Semantic Segmentation for Large-Scale Scene Understanding 2020-3D Semantic Segmentation for Large-Scale Scene3D Semantic Segmentation for Large-Scale Scene Understanding SemanticKITTI
2020-3D Semantic Segmentation for Large-Scale Scene3D Semantic Segmentation for Large-Scale Scene Understanding 2020-3D Semantic Segmentation for Large-Scale Scene3D Semantic Segmentation for Large-Scale Scene Understanding 
