nerf2mesh

This repository contains a PyTorch re-implementation of the paper: Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement.

Project Page | Arxiv | Paper | Models

Install

git clone https://github.com/ashawkey/nerf2mesh.git
cd nerf2mesh

Install with pip

pip install -r requirements.txt

# nvdiffrast
pip install git+https://github.com/NVlabs/nvdiffrast/

# pytorch3d
pip install git+https://github.com/facebookresearch/pytorch3d.git

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 raymarching
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 raymarching/ folder, since this only install the built extension.)

Tested environments

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

Usage

We support the original NeRF data format like nerf-synthetic, and COLMAP dataset like Mip-NeRF 360. Please download and put them under ./data.

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

Basics

### Stage0 (NeRF, continuous, volumetric rendering), this stage exports a coarse mesh under <workspace>/mesh_stage0/

# nerf
python main.py data/nerf_synthetic/lego/ --workspace trial_syn_lego/ -O --bound 1 --scale 0.8 --dt_gamma 0 --stage 0 --lambda_tv 1e-8

# colmap
python main.py data/garden/ --workspace trial_360_garden -O --data_format colmap --bound 16 --enable_cam_center --enable_cam_near_far --scale 0.3 --downscale 4 --stage 0 --lambda_entropy 1e-3 --clean_min_f 16 --clean_min_d 10 --lambda_tv 2e-8 --visibility_mask_dilation 50

### Stage1 (Mesh, binarized, rasterization), this stage exports a fine mesh with textures under <workspace>/mesh_stage1/

# nerf
python main.py data/nerf_synthetic/lego/ --workspace trial_syn_lego/ -O --bound 1 --scale 0.8 --dt_gamma 0 --stage 1

# colmap
python main.py data/garden/ --workspace trial_360_garden   -O --data_format colmap --bound 16 --enable_cam_center --enable_cam_near_far --scale 0.3 --downscale 4 --stage 1 --iters 10000

### Web Renderer
# you can simply open <workspace>/mesh_stage1/mesh.obj with a 3D viewer to visualize the diffuse texture.
# to render full diffuse + specular, you'll need to host this folder (e.g., by vscode live server), and open renderer.html for further instructions.

Custom Dataset

Tips:

• To get best mesh quality, you may need to adjust --scale to let the most interested object fall inside the unit box [-1, 1]^3, which can be visualized by appending --vis_pose.
• To better model background (especially for outdoor scenes), you may need to adjust --bound to let most sparse points fall into the full box [-bound, bound]^3, which can also be visualized by appending --vis_pose.
• For forward-facing dataset, remove --enable_cam_center so the scene center is determined by sparse points which is more suitable.

# 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

# recommended options for outdoor 360-inwarding captures
python main.py data/custom/ --workspace trial_custom -O --data_format colmap --bound 16 --enable_cam_center --enable_cam_near_far --scale 0.3 --stage 0 --lambda_entropy 1e-3 --clean_min_f 16 --clean_min_d 10 --lambda_tv 2e-8 --visibility_mask_dilation 50

python main.py data/custom/ --workspace trial_custom -O --data_format colmap --bound 16 --enable_cam_center --enable_cam_near_far --scale 0.3 --stage 1 --iters 10000 --lambda_normal 1e-3

# recommended options for forward-facing captures
python main.py data/custom/ --workspace trial_custom -O --data_format colmap --bound 4 --scale 0.3 --stage 0 --clean_min_f 16 --clean_min_d 10 --lambda_tv 2e-8 --visibility_mask_dilation 50

python main.py data/custom/ --workspace trial_custom -O --data_format colmap --bound 4 --scale 0.3 --stage 1 --iters 10000 --lambda_normal 1e-3

Advanced Usage

### -O: the recommended setting, equals
--fp16 --preload --mark_untrained --random_image_batch --adaptive_num_rays --refine --mesh_visibility_culling

### load checkpoint
--ckpt latest # by default we load the latest checkpoint in the workspace
--ckpt scratch # train from scratch. For stage 1, this will still load the stage 0 model as an initialization.
--ckpt trial/checkpoints/xxx.pth # specify it by path

### testing
--test # test, save video and mesh
--test_no_video # do not save video
--test_no_mesh # do not save mesh

### dataset related
--data_format [colmap|nerf] # dataset format
--enable_cam_center # use camera center instead of sparse point center as scene center (colmap dataset only)
--enable_cam_near_far # estimate camera near & far from sparse points (colmap dataset only)

--bound 16 # scene bound set to [-16, 16]^3, note that only meshes inside the center [-1, 1]^3 will be adaptively refined!
--scale 0.3 # camera scale, if not specified, automatically estimate one based on camera positions. Important targets should be scaled into the center [-1, 1]^3.

### visualization 
--vis_pose # viusalize camera poses and sparse points (sparse points are colmap dataset only)
--gui # open gui (only for testing, training in gui is not well supported!)

### balance between surface quality / rendering quality

# increase these weights to get better surface quality but worse rendering quality
--lambda_tv 1e-7 # total variation loss (stage 0)
--lambda_entropy 1e-3 # entropy on rendering weights (transparency, alpha), encourage them to be either 0 or 1 (stage 0)
--lambda_lap 0.001 # laplacian smoothness loss (stage 1)
--lambda_normal 0.001 # normal consistency loss (stage 1)
--lambda_offsets 0.1 # vertex offsets L2 loss (stage 1)
--lambda_edgelen 0.1 # edge length L2 loss (stage 1)

# set all smoothness regularizations to 0, usually get the best rendering quality
--wo_smooth

### coarse mesh extraction & post-processing
--mcubes_reso 512 # marching cubes resolution
--decimate_target 300000 # decimate raw mesh to this face number
--clean_min_d 5 # isolated floaters with smaller diameter will be removed
--clean_min_f 8 # isolated floaters with fewer faces will be removed
--visibility_mask_dilation 5 # dilate iterations after performing visibility face culling

### fine mesh exportation
--texture_size 4096 # max texture image resolution
--ssaa 2 # super-sampling anti-alias ratio
--refine_size 0.01 # finest edge len at subdivision
--refine_decimate_ratio 0.1 # decimate ratio at each refine step
--refine_remesh_size 0.02 # remesh edge len after decimation

### Depth supervision (colmap dataset only)

# generate downscaled images ('images` --> `images_4`)
python scripts/downscale.py data/<name> --downscale 4

# download depth checkpoints (omnidata v2)
cd depth_tools
bash download_models.sh
cd ..

# generate dense depth
python depth_tools/extract_depth.py --in_dir data/<name>/images_4 --out_dir data/<name>/depths

# enable dense depth training
python main.py data/<name> -O --bound 16 --data_format colmap --enable_dense_depth

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

Acknowledgement

• The NeRF framework is based on torch-ngp.
• The GUI is developed with DearPyGui.

Citation

@article{tang2022nerf2mesh,
  title={Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement},
  author={Tang, Jiaxiang and Zhou, Hang and Chen, Xiaokang and Hu, Tianshu and Ding, Errui and Wang, Jingdong and Zeng, Gang},
  journal={arXiv preprint arXiv:2303.02091},
  year={2022}
}