# ipod

**Repository Path**: alibaba/ipod

## Basic Information

- **Project Name**: ipod
- **Description**: No description available
- **Primary Language**: Unknown
- **License**: Apache-2.0
- **Default Branch**: main
- **Homepage**: None
- **GVP Project**: No

## Statistics

- **Stars**: 0
- **Forks**: 0
- **Created**: 2024-10-31
- **Last Updated**: 2026-10-01

## Categories & Tags

**Categories**: Uncategorized

**Tags**: None

## README

# IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images

[**[Paper]**](https://to-be-released) | [**[Project page]**](yushuang-wu.github.io/IPoD/)

![teaser](media/teaser.png "teaser")

This repository contains the official implementation of the paper:

**IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images**

Yushuang Wu, Luyue Shi, Junhao Cai, Weihao Yuan, Lingteng Qiu, Zilong Dong, Liefeng Bo, Shuguang Cui, Xiaoguang Han

**Accepted by CVPR 2024, Highlight**

This work was done by Yushuang Wu during intership at Alibaba Group supervised by Weihao Yuan.


## Installation
Please see [INSTALL.md](INSTALL.md) for information on installation.


## Data
Please see [DATASET.md](DATASET.md) for information on data preparation.

## Pretrained models

To download the pretrained models, run:

```
mkdir ckpts

cd ckpts

wget https://virutalbuy-public.oss-cn-hangzhou.aliyuncs.com/share/YushuangWu/IPoD_ckpts/ipod_transformer_co3d.pth

```


## CO3D-v2 Experiments

To train from scratch, run:
```
sh train.sh
```

The arguements are used the same with ones in the repository of [NU-MCC](https://github.com/sail-sg/numcc).

For evaluation/inference:

```
sh eval.sh
```

The argument `--n_query_udf` defines the total number of points in the final output. In general, the higher numbers result in more uniform point distribution and also longer inference time. 


To run visualization, use `--run_viz` flag. The output will be generated to the folder specified in `--exp_name`. Visualization/evaluation from one class can be specified using `--one_class [OBJECT_CLASS]` flag. Point clouds can be exported by activating `--save_pc` flag.

## Acknowledgement
This codebase is mainly inherited from the repositories of [NU-MCC](https://github.com/sail-sg/numcc) and [MCC](https://github.com/facebookresearch/MCC).



## Citation
If you find our code or paper useful, please consider citing us:

```bibtex
@inproceedings{wu2023ipod,
  title={IPoD: Implicit Field Learning with Point Diffusion for Generalizable 3D Object Reconstruction from Single RGB-D Images},
  author={Yushuang, Wu and Luyue,  Shi and Junhao, Cai and Weihao, Yuan and Lingteng, Qiu and Zilong, Dong and Liefeng, Bo and Shuguang, Cui and Xiaoguang, Han},
  booktitle={The IEEE/CVF Computer Vision and Pattern Recognition Conference (CVPR)},
  year={2024}
}
```