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Point-NeRF: Point-based Neural Radiance Fields
| Date | Stars |
|---|---|
| 2026-07-24 | 1154 |
| 2026-07-25 | 1154 |
| 2026-07-28 | 1154 |
| 2026-07-30 | 1154 |
| 2026-08-06 | 1154 |
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# Point-NeRF: Point-based Neural Radiance Fields (CVPR 2022 Oral 🤩)
<img src="./images/Adobe-Logos.png" width=120px /><img src="images/USC-Logos.png" width=120px />
[Project Sites](https://xharlie.github.io/projects/project_sites/pointnerf/index.html)
| [Paper](https://arxiv.org/pdf/2201.08845.pdf) |
Primary contact: [Qiangeng Xu](https://xharlie.github.io/)
Point-NeRF uses neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud; this point cloud can be finetuned to surpass the visual quality of NeRF with 30X faster training time. Point-NeRF can be combined with other 3D reconstruction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism.
<!-- <img src="./images/pipeline.png" /> -->
[](https://youtu.be/zmR9j-4AebA)
## Reference
Please cite our paper if you are interested
<strong>Point-NeRF: Point-based Neural Radiance Fields</strong>.
```
@inproceedings{xu2022point,
title={Point-nerf: Point-based neural radiance fields},
author={Xu, Qiangeng and Xu, Zexiang and Philip, Julien and Bi, Sai and Shu, Zhixin and Sunkavalli, Kalyan and Neumann, Ulrich},
booktitle={Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition},
pages={5438--5448},
year={2022}
}
```
## Updates ##
1. To replace pycuda, we have implemented the pytorch cuda functions when using world coordinates to group neural points. Simply set wcoord_query=-1
in your configuration file if the original setting is wcoord_query=1 (see dev_scripts/w_n360/chair_cuda.sh).
2. We have received constructive feedbacks that when Point-NeRF use MVSNet to reconstruct point cloud, the point fusion after depth estimation by MVSNet will use the alpha channel information in the NeRF-Synthetic Dataset. It is due to the fact that MVSNet cannot handle background very well. To improve the fairness, we include new training scripts and results of PointNeRF + MVSNet when using background color for filtering. The results (see below) are similar to the ones that are previously reported.
| | Chair | Drums | Lego | Mic | Materials | Ship | Hotdog | Ficus | Avg |
| ---- | ---- | ---- | --- | ---- | ---- | ---- | ------- | ------- |------- |
| PSNR | 35.60 | 26.04 | 35.27 | 35.91 | 29.65 | 30.61 | 37.34 | 35.61 | 33.25 |
| SSIM | 0.991 | 0.954 | 0.989 | 0.994 | 0.971 | 0.938 | 0.991 | 0.992 | 0.978 |
| LPIPSVgg | 0.023 | 0.078 | 0.021 | 0.014 | 0.071 | 0.129 | 0.036 | 0.025 | 0.050 |
| LPIPSAlex | 0.010 | 0.055 | 0.010 | 0.007 | 0.041 | 0.076 | 0.016 | 0.011 | 0.028 |
This issue only affacts situations when Point-NeRF uses MVSNet on NeRF-Synthetic Dataset. The Colmap results and results on other datasets are not impacted.
An even more reasonable reconstruction approach should exclude using the knowledge of background color or other point filtering. Therefore, we suggest users to combine PointNeRF with more powerful MVS models, such as [TransMVS](https://github.com/megvii-research/TransMVSNet).
## Overal Instruction
1. Please first install the libraries as below and download/prepare the datasets as instructed.
2. Point Initialization: Download pre-trained MVSNet as below and train the feature extraction from scratch or directly download the pre-trained models. (Obtain 'MVSNet' and 'init' folder in checkpoints folder)
3. Per-scene Optimization: Download pre-trained models or optimize from scratch as instructed.
For nerfsynthetic, colmap_nerfsynthetic, tanks&temples, scannet and dtu,
We provide all the checkpoint_files [google drive](https://drive.google.com/drive/folders/1xk1GhDhgPk1MrlX8ncfBzExcerpt of 15,288 characters
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matched fp:bf425f8e0c950921, topic:nerf, topic:3d-reconstruction, readme:computer vision