Code for "OctField: Hierarchical Implicit Functions for 3D Modeling (NeurIPS 2021)"

Related tags

Deep Learning OctField
Overview

OctField(Jittor): Hierarchical Implicit Functions for 3D Modeling

Introduction

This repository is code release for OctField: Hierarchical Implicit Functions for 3D Modeling (arXiv pdf here).

OctField utilizes an octree structure to achieve a hierarchical implicit representation, where part geometry enclosed by an octant is reperesented by a local implicit function. In this repository, we provide OctField model implementation (with Jittor) as well as data preparation, training and testing scripts on ShapeNet.

Installation

The code is tested with Ubuntu 18.04, Python 3.7, Jittor 1.3.1.22, CUDA 10.1 and cuDNN v7.5.

Install the following Python dependencies (with pip install): h5py trimesh scipy scikit-learn scikit-image pybind11 tensorboardX

For the jittor installation, please refer to this link.

Data preprocessing

1. Compile imp_sampling according to the steps in the imp_sampling/ReadMe.txt and copy the .so file into preproces/
2. Using normal_tool.py, sample_tool.py, and voxelize_tool.py to compute the voxels and sampled points.
3. Using pkg_part.py to get the input .h5 file

Training

Run train.sh to train the model.

Testing

Run eval_recon.py to achieve the reconstruction result.

Citation

If you find our work useful in your research, please consider citing:

@inproceedings{tang2021octfield,
    author = {Jia-Heng Tang and Weikai Chen and Jie Yang and Bo Wang and Songrun Liu and Bo Yang and Lin Gao},
    title = {OctField: Hierarchical Implicit Functions for 3D Modeling},
    booktitle = {The Thirty-Fifth Annual Conference on Neural Information Processing Systems (NeurIPS)},
    year = {2021}
}
Comments
  • bug

    bug

    pkg_part.py里生成.h5文件代码中又有点问题，这里的data_path是存放什么数据的 image 这里的npz文件是之前的哪一步生成的，之前的sample_tool生成的npz文件键值对应不上 image

    Originally posted by @zouwenqin in https://github.com/IGLICT/OctField/issues/5#issuecomment-1004479083

    opened by zouwenqin 2
  • Did you do a fair comparison with other methods in Table 2?

    Did you do a fair comparison with other methods in Table 2?

    The OccNet and ConvOccNet are all trained on 13 categories of ShapeNet. According to your code, your network is trained on each category separately. For the results of ConvOccNet as shown in Table 2, why are the Chamfer Distances of ConvONet even worse than the results trained on 13 categories as reported in the paper of ConvOccNet? And ConvOccNet is trained using sparse and noisy point clouds (3000 points) as input. However, your network is trained with clean point clouds as shown in Figure 4-(a). How can you do the comparison like this? Is it a fair comparison?

    opened by abc-def-g 1
  • Target file not found after compilation

    Target file not found after compilation

    Hi, Thank you for releasing the code! :) I'm trying to run the code and I've successfully compiled imp_sampling. However, I cannot find the file "generateSamplesWind" after compilation and I cannot find any code related to generateSamplesWind in imp_sampling/build/Makefile. https://github.com/IGLICT/OctField/blob/e776b3bc8a0abcf5c8d875cb679647fcaef33c36/imp_sampling/ReadMe.txt#L6 Would you please check this part? Another question is: may I transform the .sdf file to a mesh directly? Would you please release the code if you have it? Thank you!

    opened by xmeng525 1
  • Code of model missing

    Code of model missing

    Hi, authors, thanks for your great work. I found that the implementation of models.RecursiveEncoder is missing. It looks like the models.py file is missing.

    opened by walsvid 1
  • [requests] Do you have an implement of IMNet using jittor?

    [requests] Do you have an implement of IMNet using jittor?

    opened by Bailey-24 0
  • CUDA issues

    CUDA issues

    Thank you for your work and for open-sourcing the code! I am trying to run your pipeline, but I am running into a couple of issues. I have followed the instructions from the repo to set up the code as well as the jittor instructions to enable CUDA support.

    First, it seems like train_part.py isn't using CUDA, and it's running very slowly on my machine (1% in ~8 hours). If I add jt.flags.use_cuda = 1 I get the follwoing error:

    python3.7: symbol lookup error: /home/ubuntu/.cache/jittor/jt1.3.4/g++7.5.0/py3.7.13/Linux-5.4.0-10xfe/IntelRCoreTMi9x9f/default/cu11.2.152_sm_86/jit/cudnn_conv3d_Tx_bool__Ty_float32__Tw_float32__JIT_1__JIT_cuda_1__index_t_int32__hash_b2c2838050d33db1_op.so: undefined symbol: _ZN6jittor11getDataTypeIbEE15cudnnDataType_tv

    On the other hand, I can run python3.7 -m jittor.test.test_cudnn_op and python3.7 -m jittor.test.test_resnet without any errors.

    Any ideas? Thanks for the help!

    opened by RaresAmbrus 0
  • Metrics

    Metrics

    Hi, Thanks for your great work. I want to know how you compute metrics in the Shape Reconstruction experiment? Spercifically, to compute CD or EMD, how many points do you sample from reconstructed and gt mesh? Do you use L1 or L2 distance? "CD = one_direction + another_direction" or "CD = 0.5 * (one_direction + another_direction)"?

    opened by random649 0
Owner
Intelligent Graphics Laboratory, Institute of Computing Technology
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