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Segment-Anything NeRF

🎉🎉🎉 Welcome to the Segment-Anything NeRF GitHub repository! 🎉🎉🎉

Segment-Anything NeRF is a novel approach for performing segmentation in a Neural Radiance Fields (NeRF) framework. Our approach renders the semantic feature of a certain view directly, eliminating the need for the forward process of the backbone of the segmentation model. By leveraging the light-weight SAM decoder, we can achieve interactive 3D-consistent segmentation at 5 FPS (rendering 512x512 image) on a V100.

interactive_seg.mp4

open_vocabulary_seg.mp4

News

[2023/4/29] Add a demo of Open-Vocabulary Segmentation in NeRF based on X-Decoder.

Key features

Learn 3D consistent SAM backbone features along with RGB and density, so we can bypass the ViT-Huge encoder and use ray marching to produce SAM features efficiently.
Online distillation with camera augmentation and caching for robust and fast training (~1 hour per scene for two stages on a V100).

NOTE: This is a work in progress, more demonstration (e.g., open-vocabulary segmentation) and a technical report is on the way!

Install

git clone https://github.com/ashawkey/Segment-Anything-NeRF.git
cd Segment-Anything-NeRF

# download SAM ckpt
mkdir pretrained && cd pretrained
wget https://dl.fbaipublicfiles.com/segment_anything/sam_vit_h_4b8939.pth

Install with pip

pip install -r requirements.txt

Build extension (optional)

By default, we use load to build the extension at runtime. However, this may be inconvenient sometimes. Therefore, we also provide the setup.py to build each extension:

# install all extension modules
bash scripts/install_ext.sh

# if you want to install manually, here is an example:
cd gridencoder
python setup.py build_ext --inplace # build ext only, do not install (only can be used in the parent directory)
pip install . # install to python path (you still need the gridencoder/ folder, since this only install the built extension.)

Tested environments

Ubuntu 22 with torch 1.12 & CUDA 11.6 on a V100.

Usage

We majorly support COLMAP dataset like Mip-NeRF 360. Please download and put them under ./data.

For custom datasets:

# prepare your video or images under /data/custom, and run colmap (assumed installed):
python scripts/colmap2nerf.py --video ./data/custom/video.mp4 --run_colmap # if use video
python scripts/colmap2nerf.py --images ./data/custom/images/ --run_colmap # if use images

First time running will take some time to compile the CUDA extensions.

### train rgb
python main.py data/garden/ --workspace trial_garden --enable_cam_center --downscale 4

### train sam features
# --with_sam: enable sam prediction
# --init_ckpt: specify the latest checkpoint from rgb training
python main.py data/garden/ --workspace trial2_garden --enable_cam_center --downscale 4 --with_sam --init_ckpt trial_garden/checkpoints/ngp.pth --iters 5000

### test sam (interactive GUI, recommended!)
# left drag & middle drag & wheel scroll: move camera
# right click: add/remove point marker
# NOTE: only square images are supported for now!
python main.py data/garden/ --workspace trial2_garden --enable_cam_center --downscale 4 --with_sam --init_ckpt trial_garden/checkpoints/ngp.pth --test --gui

# test sam (without GUI, random points query)
python main.py data/garden/ --workspace trial2_garden --enable_cam_center --downscale 4 --with_sam --init_ckpt trial_garden/checkpoints/ngp.pth --test

Please check the scripts directory for more examples on common datasets, and check main.py for all options.

Acknowledgement

Segment-Anything:

@article{kirillov2023segany,
    title={Segment Anything},
    author={Kirillov, Alexander and Mintun, Eric and Ravi, Nikhila and Mao, Hanzi and Rolland, Chloe and Gustafson, Laura and Xiao, Tete and Whitehead, Spencer and Berg, Alexander C. and Lo, Wan-Yen and Doll{\'a}r, Piotr and Girshick, Ross},
    journal={arXiv:2304.02643},
    year={2023}
}

X-Decoder:

@article{zou2022generalized,
  title={Generalized Decoding for Pixel, Image, and Language},
  author={Zou, Xueyan and Dou, Zi-Yi and Yang, Jianwei and Gan, Zhe and Li, Linjie and Li, Chunyuan and Dai, Xiyang and Behl, Harkirat and Wang, Jianfeng and Yuan, Lu and others},
  journal={arXiv preprint arXiv:2212.11270},
  year={2022}
}

Citation

If you find this work useful, a citation will be appreciated via:

@misc{segment-anything-nerf,
    Author = {Jiaxiang Tang and Xiaokang Chen and Diwen Wan and Jingbo Wang and Gang Zeng},
    Year = {2023},
    Note = {https://github.com/ashawkey/Segment-Anything-NeRF},
    Title = {Segment-Anything NeRF}
}

	with torch.no_grad():
	if use_cache:
	gt_samvit = data['gt_samvit']
	else:
	# render high-res RGB
	outputs = self.model.render(rays_o, rays_d, staged=True, index=index, bg_color=bg_color, perturb=True, cam_near_far=cam_near_far, update_proposal=False, return_feats=0)
	pred_rgb = outputs['image'].reshape(H, W, 3)

	# encode SAM ground truth
	image = (pred_rgb.detach().cpu().numpy() * 255).astype(np.uint8)
	self.sam_predictor.set_image(image)
	gt_samvit = self.sam_predictor.features # [1, 256, 64, 64]

	# write to cache
	if self.opt.cache_size > 0:
	data['gt_samvit'] = gt_samvit
	self.cache.insert(data)

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segment-anything-nerf's Introduction

Segment-Anything NeRF

News

Key features

Install

Install with pip

Build extension (optional)

Tested environments

Usage

Acknowledgement

Citation

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