Implementation of HiFANet

1. Paper: Yuhang He, Lin Chen, Junkun Xie, Long Chen, Learning 3D Semantics from Pose-Noisy 2D Images with Hierarchical Full Attention Network. arXiv

2. Idea Summary: We propose to infer 3D point cloud semantics by aggregating 2D image semantics from temporal sequential observations. We consider LiDAR-camera pose error which is common in real scenario by proposing the HiFANet. HiFANet is pose-noise tolerant and light-weight. It hierarchically aggregates patch-level, instance-level and point-level semantics.

3. Implementation with Pytorch. After preparing the training dataset (see paper for details), run main.py to train the model.

4. If you find our work helpful, please cite as:

@misc{HiFANet,
  doi = {10.48550/ARXIV.2204.08084},
  url = {https://arxiv.org/abs/2204.08084},
  author = {He, Yuhang and Chen, Lin and Xie, Junkun and Chen, Long},
  title = {Learning 3D Semantics from Pose-Noisy 2D Images with Hierarchical Full Attention Network},
  year = {2022},
}