# holiday **Repository Path**: Roc233/holiday ## Basic Information - **Project Name**: holiday - **Description**: No description available - **Primary Language**: Unknown - **License**: MIT - **Default Branch**: master - **Homepage**: None - **GVP Project**: No ## Statistics - **Stars**: 0 - **Forks**: 0 - **Created**: 2020-05-13 - **Last Updated**: 2020-12-19 ## Categories & Tags **Categories**: Uncategorized **Tags**: None ## README <<<<<<< HEAD # 3D-LMNet This repository contains the source codes for the paper [3D-LMNet: Latent Embedding Matching For Accurate and Diverse 3D Point Cloud Reconstruction From a Single Image](https://arxiv.org/abs/1807.07796).
Accepted at *British Machine Vision Conference (BMVC 2018)* ## Citing this work If you find this work useful in your research, please consider citing: ``` @inproceedings{mandikal20183dlmnet, author = {Mandikal, Priyanka and Navaneet, K L and Agarwal, Mayank and Babu, R Venkatesh}, booktitle = {Proceedings of the British Machine Vision Conference ({BMVC})}, title = {{3D-LMNet}: Latent Embedding Matching for Accurate and Diverse 3D Point Cloud Reconstruction from a Single Image}, year = {2018} } ``` ## Overview 3D-LMNet is a latent embedding matching approach for 3D point cloud reconstruction from a single image. To better incorporate the data prior and generate meaningful reconstructions, we first train a 3D point cloud auto-encoder and then learn a mapping from the 2D image to the corresponding learnt embedding. For a given image, there may exist multiple plausible 3D reconstructions depending on the object view. To tackle the issue of uncertainty in the reconstruction, we predict multiple reconstructions that are consistent with the input view, by learning a probablistic latent space using a view-specific ‘diversity loss’. We show that learning a good latent space of 3D objects is essential for the task of single-view 3D reconstruction. ![Overview of 3D-LMNet](images/approach_overview.png) ## Dataset ### ShapeNet We train and validate our model on the ShapeNet dataset. We use the rendered images from the dataset provided by 3d-r2n2, which consists of 13 object categories. For generating the ground truth point clouds, we sample points on the corresponding object meshes from ShapeNet. We use the dataset split provided by r2n2 in all the experiments. Data download links are provided below:
Rendered Images (~12.3 GB): http://cvgl.stanford.edu/data2/ShapeNetRendering.tgz
ShapeNet pointclouds (~2.8 GB): https://drive.google.com/open?id=1cfoe521iTgcB_7-g_98GYAqO553W8Y0g
ShapeNet train/val split: https://drive.google.com/open?id=10FR-2Lbn55POB1y47MJ12euvobi6mgtc Download each of the folders, extract them and move them into *data/shapenet/*.
The folder structure should now look like this:
--data/shapenet/
  --ShapeNetRendering/
  --ShapeNet_pointclouds/
  --splits/
### Pix3D We evaluate the generalization capability of our model by testing it on the real-world pix3d dataset. For the ground truth point clouds, we sample 1024 points on the provided meshes. Data download links are provided below:
Pix3D dataset (~20 GB): Follow the instructions in https://github.com/xingyuansun/pix3d
Pix3D pointclouds (~13 MB): https://drive.google.com/open?id=1RZakyBu9lPbG85SyconBn4sR8r2faInV Download each of the folders, extract them and move them into *data/pix3d/*.
The folder structure should now look like this:
--data/pix3d/
  --img_cleaned_input/
  --img/
  --mask/
  --model/
  --pix3d_pointclouds/
  --pix3d.json
## Usage Install [TensorFlow](https://www.tensorflow.org/install/). We recommend version 1.3 so that the additional TensorFlow ops can be compiled. The code provided has been tested with Python 2.7, TensorFlow 1.3, and CUDA 8.0. The following steps need to be performed to run the codes given in this repository: 1. Clone the repository: ```shell git clone https://github.com/val-iisc/3d-lmnet.git cd 3d-lmnet ``` 2. Tensorflow ops for losses (Chamfer and EMD) as well as for point cloud visualization need to be compiled. Run the makefile as given below. (Note that the the nvcc, cudalib, and tensorflow paths inside the makefile need to be updated to point to the locations on your machine): ```shell make ``` ## Training - To train the point-cloud auto-encoder, run: ```shell bash scripts/train_ae.sh ``` Note that the auto-encoder needs to be trained before training either of the latent matching setups. - To train the latent matching (lm) setup, run: ```shell bash scripts/train_lm.sh ``` - To train the probabilistic latent matching (plm) setup, run: ```shell bash scripts/train_plm.sh ``` ## Trained Models Create a folder called 'trained_models' inside the project folder: ``` mkdir trained_models ``` - Download the trained model for latent matching (lm) here:
[https://drive.google.com/open?id=1nl30z1CJL5WZn8svFllHkVLvS4GWiAxf](https://drive.google.com/open?id=1nl30z1CJL5WZn8svFllHkVLvS4GWiAxf)
Extract it and move it into *trained_models/* - Download the trained model for probabilistic latent matching (plm) here:
[https://drive.google.com/open?id=1iYUOPTrhwAIwubihrBhLGG-KVD4Qr7is](https://drive.google.com/open?id=1iYUOPTrhwAIwubihrBhLGG-KVD4Qr7is)
Extract it and move it into *trained_models/* ## Evaluation Follow the steps detailed above to download the dataset and pre-trained models. ### ShapeNet - For computing the Chamfer and EMD metrics reported in the paper (all 13 categories), run: ```shell bash scripts/metrics_shapenet_lm.sh ``` The computed metrics will be saved inside *trained_models/lm/metrics_shapenet/* - For the plm setup (chair category), run: ```shell bash scripts/metrics_shapenet_plm.sh ``` The computed metrics will be saved inside *trained_models/plm/metrics_shapenet/* ### Pix3D - For computing the Chamfer and EMD metrics reported in the paper (3 categories) for the real-world Pix3D dataset, run: ```shell bash scripts/metrics_pix3d.sh ``` The computed metrics will be saved inside *trained_models/lm/metrics_pix3d/* ## Demo Follow the steps detailed above to download the dataset and pre-trained models. ### ShapeNet - Run the following to visualize the results for latent matching (lm): ```shell bash scripts/demo_shapenet_lm.sh ``` You can navigate to the next visualization by pressing 'q'. Close visualization using back arrow. You can visualize results for different categories by changing the value of the category flag. - Run the following to visualize the results for probabilistic latent matching (plm): ```shell bash scripts/demo_shapenet_plm.sh ``` ### Pix3D - Run the following to visualize the results on the real-world Pix3D dataset: ```shell bash scripts/demo_pix3d.sh ``` You can navigate to the next visualization by pressing 'q'. Close visualization using back arrow. ## Sample Results ### ShapeNet Below are a few sample reconstructions from our trained model tested on ShapeNet. ![3D-LMNet_ShapeNet_results](images/shapenet_results.png) ### Pix3D Below are a few sample reconstructions from our trained model tested on real-world Pix3D dataset. Note that we mask out the background using the provided masks before passing the images through the network. ![3D-LMNet_Pix3D_results](images/pix3d_results.png) ======= # holiday #### 介绍 {**以下是码云平台说明，您可以替换此简介** 码云是 OSCHINA 推出的基于 Git 的代码托管平台（同时支持 SVN）。专为开发者提供稳定、高效、安全的云端软件开发协作平台 无论是个人、团队、或是企业，都能够用码云实现代码托管、项目管理、协作开发。企业项目请看 [https://gitee.com/enterprises](https://gitee.com/enterprises)} #### 软件架构 软件架构说明 #### 安装教程 1. xxxx 2. xxxx 3. xxxx #### 使用说明 1. xxxx 2. xxxx 3. xxxx #### 参与贡献 1. Fork 本仓库 2. 新建 Feat_xxx 分支 3. 提交代码 4. 新建 Pull Request #### 码云特技 1. 使用 Readme\_XXX.md 来支持不同的语言，例如 Readme\_en.md, Readme\_zh.md 2. 码云官方博客 [blog.gitee.com](https://blog.gitee.com) 3. 你可以 [https://gitee.com/explore](https://gitee.com/explore) 这个地址来了解码云上的优秀开源项目 4. [GVP](https://gitee.com/gvp) 全称是码云最有价值开源项目，是码云综合评定出的优秀开源项目 5. 码云官方提供的使用手册 [https://gitee.com/help](https://gitee.com/help) 6. 码云封面人物是一档用来展示码云会员风采的栏目 [https://gitee.com/gitee-stars/](https://gitee.com/gitee-stars/) >>>>>>> bb4086058a0cb488e5c30f5337f07f35ee9c0cea