PALNet
Code and Data for "Depth Based Semantic Scene Completion with Position Importance Aware Loss", ICRA2020 and RAL
Install / Use
/learn @UniLauX/PALNetREADME
Depth Based Semantic Scene Completion with Position Importance Aware Loss
By Yu Liu*, Jie Li*, Xia Yuan, Chunxia Zhao, Roland Siegwart, Ian Reid and Cesar Cadena (* indicates equal contribution)
ICRA2020 In Conjunction of RAL
Video Demo:
https://youtu.be/j-LAMcMh0yg
Requirements:
python 2.7
pytorch 0.4.1
CUDA 8
Testing
python ./test.py
--data_test=/path/to/NYUCADtest
--batch_size=1
--workers=4
--resume='PALNet_weights.pth.tar'
Weights
Datasets
The original dataset is from SSCNet
Here is the NYUCAD data reproduced from SSCNet for a quick demo.
Adelaide AI Group
more work from Adelaide can be found in: https://github.com/Adelaide-AI-Group/Adelaide-AI-Group.github.io
