Top AI Repos — open-source AI, indexed and scored
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
Top AI Repos tracks AI repositories on GitHub and answers two different questions about each one: is it moving right now, and would you bet a product on it.
PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling （CVPR 2020）
| Date | Stars |
|---|---|
| 2026-07-24 | 268 |
| 2026-07-25 | 268 |
| 2026-07-28 | 268 |
| 2026-07-30 | 268 |
| 2026-07-31 | 268 |
| 2026-08-06 | 268 |
Today
— stars today
This week
— stars this week
This month
— stars this month
Momentum
0.0
growth rate 0.00%/day
# PointASNL
This repository is for **PointASNL** introduced in the following paper
Xu Yan, [Chaoda Zheng](https://github.com/Ghostish), [Zhen Li*](https://mypage.cuhk.edu.cn/academics/lizhen/), Sheng Wang and Shuguang Cui, "PointASNL: Robust Point Clouds Processing using Nonlocal Neural Networks with Adaptive Sampling", CVPR 2020 [[arxiv]](https://arxiv.org/pdf/2003.00492.pdf).
If you find our work useful in your research, please consider citing:
```
@inproceedings{yan2020pointasnl,
title={Pointasnl: Robust point clouds processing using nonlocal neural networks with adaptive sampling},
author={Yan, Xu and Zheng, Chaoda and Li, Zhen and Wang, Sheng and Cui, Shuguang},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5589--5598},
year={2020}
}
```
## Getting Started
### (1) Set up
Clone the repository:
```
git clone https://github.com/yanx27/PointASNL.git
```
Installation instructions for Ubuntu 16.04 (available at **CUDA10**):
* Make sure <a href="https://docs.nvidia.com/cuda/cuda-installation-guide-linux/index.html">CUDA</a> and <a href="https://docs.nvidia.com/deeplearning/sdk/cudnn-install/index.html">cuDNN</a> are installed. Only this configurations has been tested:
- Python 3.6.9, TensorFlow 1.13.1, CUDA 10.1
* Follow <a href="https://www.tensorflow.org/install/pip">Tensorflow installation procedure</a>.
* Compile the customized Tensorflow operators by `sh complile_op.sh`.
N.B. If you installed Tensorflow in a virtual environment, it needs to be activated when running these scripts
### (2) ModelNet40 Classification
Aligned ModelNet40 dataset can be found [here](https://shapenet.cs.stanford.edu/media/modelnet40_normal_resampled.zip). Since the randomness of data augmentation, the result of this code maybe slightly different from the result in paper, but it should be around 93%.
#### Data without Noise
It will cost relatively long time in first epoch for cache construction.
```
# Training
$ python train.py --data [MODELNET40 PATH] --exp_dir PointASNL_without_noise
# Evaluation
$ python test.py --data [MODELNET40 PATH] --model_path log/PointASNL_without_noise/best_model.ckpt
```
#### Data with Noise
Model with AS module is extremely robust for noisy data. You can use adaptive sampling by setting `--AS` .
```
# Training
$ python train.py --data [MODELNET40 PATH] --exp_dir PointASNL_with_noise --AS
# Evaluation on noisy data
$ python test.py --data [MODELNET40 PATH] --model_path log/PointASNL_with_noise/best_model.ckpt --AS --noise
```
### (3) ScanNet Segmentation
We provide two options for training on ScanNet dataset (with or without pre/post processing).
With grid sampling processing, more input points and deeper network structure, our PointASNL can achieve **66.6%** on ScanNet [benchmark](http://kaldir.vc.in.tum.de/scannet_benchmark/).
#### Data Preparation
Official ScanNet dataset can be downloaded [here](http://www.scan-net.org/).
If you choose training without grid sampling, you need firstly run `ScanNet/prepare_scannet.py`, otherwise you can skip to training step.
#### Data without Processing
This method converges relatively slower, and will achieve result around 63%.
```
# Training
$ cd ScanNet/
$ python train_scannet.py --data [SCANNET PATH] --log_dir PointASNL
# Evaluation
$ cd ScanNet/
$ python test_scannet.py --data [SCANNET PATH] --model_path log/PointASNL/latest_model.ckpt
```
#### Data with Grid Sampling
We highly recommend training with this method, although it takes a long time to process the raw data, it can achieve results around 66% and will be faster to converge. Grid sampling pre-processing will be automatically conducted before training.
```
# Training
$ cd ScanNet/
$ python train_scannet_grid.py --data [SCANNET PATH] --log_dir PointASNL_grid --num_point 10240 --model pointasnl_sem_seg_res --in_radius 2
# Evaluation
$ cd ScanNet/
$ python test_scannet_grid.py --data Excerpt of 5,947 characters
Read on GitHubWould you bet a product on this? Bounded 0–100 and slow moving.
matched fp:e9f44b1536a12528, topic:tensorflow