NeRF-MAE / README.md

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	---
	pipeline_tag: feature-extraction
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	<!-- # NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields -->

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	### [Project Page](https://nerf-mae.github.io/) \| [arXiv](https://arxiv.org/abs/2308.12967) \| [PDF](https://arxiv.org/pdf/2308.12967.pdf)



	NeRF-MAE : Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields

	<a href="https://zubairirshad.com"><strong>Muhammad Zubair Irshad</strong></a>
	·
	<a href="https://zakharos.github.io/"><strong>Sergey Zakharov</strong></a>
	·
	<a href="https://www.linkedin.com/in/vitorguizilini"><strong>Vitor Guizilini</strong></a>
	·
	<a href="https://adriengaidon.com/"><strong>Adrien Gaidon</strong></a>
	·
	<a href="https://faculty.cc.gatech.edu/~zk15/"><strong>Zsolt Kira</strong></a>
	·
	<a href="https://www.tri.global/about-us/dr-rares-ambrus"><strong>Rares Ambrus</strong></a>
	<br> European Conference on Computer Vision, ECCV 2024<br>

	<b> Toyota Research Institute   \|   Georgia Institute of Technology</b>

	## 💡 Highlights
	- NeRF-MAE: The first large-scale pretraining utilizing Neural Radiance Fields (NeRF) as an input modality. We pretrain a single Transformer model on thousands of NeRFs for 3D representation learning.
	- NeRF-MAE Dataset: A large-scale NeRF pretraining and downstream task finetuning dataset.

	## 🏷️ TODO 🚀

	- [x] Release large-scale pretraining code 🚀
	- [x] Release NeRF-MAE dataset comprising radiance and density grids 🚀
	- [x] Release 3D object detection finetuning and eval code 🚀
	- [x] Pretrained NeRF-MAE checkpoints and out-of-the-box model usage 🚀

	## NeRF-MAE Model Architecture
	<p align="center">
	<img src="demo/nerf-mae_architecture.jpg" width="90%">
	</p>


	## Citation

	If you find this repository or our dataset useful, please star ⭐ this repository and consider citing 📝:

	```
	@inproceedings{irshad2024nerfmae,
	title={NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance Fields},
	author={Muhammad Zubair Irshad and Sergey Zakharov and Vitor Guizilini and Adrien Gaidon and Zsolt Kira and Rares Ambrus},
	booktitle={European Conference on Computer Vision (ECCV)},
	year={2024}
	}
	```

	### Contents
	- [🌇 Environment](#-environment)
	- [⛳ Model Usage and Checkpoints](#-model-usage-and-checkpoints)
	- [🗂️ Dataset](#-dataset)

	## 🌇 Environment

	Create a python 3.7 virtual environment and install requirements:

	```bash
	cd $NeRF-MAE repo
	conda create -n nerf-mae python=3.9
	conda activate nerf-mae
	pip install --upgrade pip
	pip install -r requirements.txt
	pip install torch==1.12.1+cu113 torchvision==0.13.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
	```
	The code was built and tested on cuda 11.3

	Compile CUDA extension, to run downstream task finetuning, as described in [NeRF-RPN](https://github.com/lyclyc52/NeRF_RPN):

	```bash
	cd $NeRF-MAE repo
	cd nerf_rpn/model/rotated_iou/cuda_op
	python setup.py install
	cd ../../../..

	```

	## ⛳ Model Usage and Checkpoints

	- [Hugginface repo to download pretrained and finetuned checkpoints](https://huggingface.co/mirshad7/NeRF-MAE)

	NeRF-MAE is structured to provide easy access to pretrained NeRF-MAE models (and reproductions), to facilitate use for various downstream tasks. This is for extracting good visual features from NeRFs if you don't have resources for large-scale pretraining. Our pretraining provides an easy-to-access embedding of any NeRF scene, which can be used for a variety of downstream tasks in a straightforwaed way.

	We have released pretrained and finetuned checkpoints to start using our codebase out-of-the-box. There are two types of usages. 1. Most common one is using the features directly in a downstream task such as an FPN head for 3D Object Detection and 2. Reconstruct the original grid for enforcing losses such as masked reconstruction loss. Below is a sample useage of our model with spelled out comments.


	1. Get the features to be used in a downstream task

	```python
	import torch

	# Define Swin Transformer configurations
	swin_config = {
	"swin_t": {"embed_dim": 96, "depths": [2, 2, 6, 2], "num_heads": [3, 6, 12, 24]},
	"swin_s": {"embed_dim": 96, "depths": [2, 2, 18, 2], "num_heads": [3, 6, 12, 24]},
	"swin_b": {"embed_dim": 128, "depths": [2, 2, 18, 2], "num_heads": [3, 6, 12, 24]},
	"swin_l": {"embed_dim": 192, "depths": [2, 2, 18, 2], "num_heads": [6, 12, 24, 48]},
	}

	# Set the desired backbone type
	backbone_type = "swin_s"
	config = swin_config[backbone_type]

	# Initialize Swin Transformer model
	model = SwinTransformer_MAE3D_New(
	patch_size=[4, 4, 4],
	embed_dim=config["embed_dim"],
	depths=config["depths"],
	num_heads=config["num_heads"],
	window_size=[4, 4, 4],
	stochastic_depth_prob=0.1,
	expand_dim=True,
	resolution=resolution,
	)

	# Load checkpoint and remove unused layers
	checkpoint_path = hf_hub_download(repo_id="mirshad7/NeRF-MAE", filename="nerf_mae_pretrained.pt")
	checkpoint = torch.load(checkpoint_path, map_location="cpu")
	model.load_state_dict(checkpoint["state_dict"])
	for attr in ["decoder4", "decoder3", "decoder2", "decoder1", "out", "mask_token"]:
	delattr(model, attr)

	# Extract features using Swin Transformer backbone. input_grid has sample shape torch.randn((1, 4, 160, 160, 160))
	features = []
	input_grid = model.patch_partition(input_grid) + model.pos_embed.type_as(input_grid).to(input_grid.device).clone().detach()
	for stage in model.stages:
	input_grid = stage(input_grid)
	features.append(torch.permute(input_grid, [0, 4, 1, 2, 3]).contiguous()) # Format: [N, C, H, W, D]

	#Multi-scale features have shape: [torch.Size([1, 96, 40, 40, 40]), torch.Size([1, 192, 20, 20, 20]), torch.Size([1, 384, 10, 10, 10]), torch.Size([1, 768, 5, 5, 5])]

	# Process features through FPN
	```

	2. Get the Original Grid Output
	```python
	import torch
	# Load data from the specified folder and filename with the given resolution.
	res, rgbsigma = load_data(folder_name, filename, resolution=args.resolution)

	# rgbsigma has sample shape torch.randn((1, 4, 160, 160, 160))

	# Build the model using provided arguments.
	model = build_model(args)

	# Load checkpoint if provided.
	if args.checkpoint:
	model.load_state_dict(torch.load(args.checkpoint, map_location="cpu")["state_dict"])
	model.eval() # Set model to evaluation mode.

	# Run inference getting the features out for downsteam usage
	with torch.no_grad():
	pred = model([rgbsigma], is_eval=True)[3] # Extract only predictions.

	```

	### 1. How to plug these features for downstream 3D bounding detection from NeRFs (i.e. plug-and-play with a [NeRF-RPN](https://github.com/lyclyc52/NeRF_RPN) OBB prediction head)

	Please also see the section on [Finetuning](#-finetuning). Our released finetuned checkpoint achieves state-of-the-art on 3D object detection in NeRFs. To run evaluation using our finetuned checkpoint on the dataset provided by NeRF-RPN, please run the below script, after updating the paths to the pretrained checkpoint i.e. --checkpoint and DATA_ROOT depending on evaluation done for ```Front3D``` or ```Scannet```:

	```
	bash test_fcos_pretrained.sh
	```

	Also see the cooresponding run file i.e. ```run_fcos_pretrained.py``` and our model adaptation i.e. ```SwinTransformer_FPN_Pretrained_Skip```. This is a minimal adaptation to plug and play our weights with a NeRF-RPN architecture and achieve significant boost in performance.


	## 🗂️ Dataset

	Download the preprocessed datasets here.

	- Pretraining dataset (comprising NeRF radiance and density grids). [Download link](https://s3.amazonaws.com/tri-ml-public.s3.amazonaws.com/github/nerfmae/NeRF-MAE_pretrain.tar.gz)
	- Finetuning dataset (comprising NeRF radiance and density grids and bounding box/semantic labelling annotations). [3D Object Detection (Provided by NeRF-RPN)](https://drive.google.com/drive/folders/1q2wwLi6tSXu1hbEkMyfAKKdEEGQKT6pj), [3D Semantic Segmentation (Coming Soon)](), [Voxel-Super Resolution (Coming Soon)]()


	Extract pretraining and finetuning dataset under ```NeRF-MAE/datasets```. The directory structure should look like this:

	```
	NeRF-MAE
	├── pretrain
	│ ├── features
	│ └── nerfmae_split.npz
	└── finetune
	└── front3d_rpn_data
	├── features
	├── aabb
	└── obb
	```


	Note: The above datasets are all you need to train and evaluate our method. Bonus: we will be releasing our multi-view rendered posed RGB images from FRONT3D, HM3D and Hypersim as well as Instant-NGP trained checkpoints soon (these comprise over 1M+ images and 3k+ NeRF checkpoints)

	Please note that our dataset was generated using the instruction from [NeRF-RPN](https://github.com/lyclyc52/NeRF_RPN) and [3D-CLR](https://vis-www.cs.umass.edu/3d-clr/). Please consider citing our work, NeRF-RPN and 3D-CLR if you find this dataset useful in your research.

	Please also note that our dataset uses [Front3D](https://arxiv.org/abs/2011.09127), [Habitat-Matterport3D](https://arxiv.org/abs/2109.08238), [HyperSim](https://github.com/apple/ml-hypersim) and [ScanNet](https://www.scan-net.org/) as the base version of the dataset i.e. we train a NeRF per scene and extract radiance and desnity grid as well as aligned NeRF-grid 3D annotations. Please read the term of use for each dataset if you want to utilize the posed multi-view images for each of these datasets.

	### For More details, please checkout out Paper, Github and Project Page!

	---
	license: cc-by-nc-4.0
	---