UniLauX / PALNet

Code and Data for "Depth Based Semantic Scene Completion with Position Importance Aware Loss", ICRA2020 and RAL

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

Model trained on NYUCAD

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