NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction

[Project] [Paper]

NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction

Yiming Wang*, Qin Han*, Marc Habermann, Kostas Daniilidis, Christian Theobalt, Lingjie Liu

ICCV 2023

NeuS2 is a method for fast neural surface reconstruction, which achieves two orders of magnitude improvement in terms of acceleration without compromising reconstruction quality, compared to NeuS. To accelerate the training process, we integrate multi-resolution hash encodings into a neural surface representation and implement our whole algorithm in CUDA. In addition, we extend our method for reconstructing dynamic scenes with an incremental training strategy.

This project is an extension of Instant-NGP enabling it to model neural surface representation and dynmaic scenes. We extended:

dependencies/neus2_TCNN
- add second-order derivative backpropagation computation for MLP;
- add progressive training for Grid Encoding.
neural-graphics-primitives
- extend NeRF mode for NeuS;
- add support for dynamic scenes.

Installation

Please first see Instant-NGP for original requirements and compilation instructions. NeuS2 follows the installing steps of Instant-NGP.

Clone this repository and all its submodules using the following command:

git clone --recursive https://github.com/19reborn/NeuS2
cd NeuS2

Then use CMake to build the project:

cmake . -B build
cmake --build build --config RelWithDebInfo -j

For python useage, first install dependencies with conda and pip:

conda create -n neus2 python=3.9
conda activate neus2
pip install -r requirements.txt

Then check https://pytorch.org/ for pytorch installation, and https://github.com/facebookresearch/pytorch3d for pytorch3d installation.

If you meet problems of compiling, you may find solutions in https://github.com/NVlabs/instant-ngp#troubleshooting-compile-errors.

Training

Static Scene

You can specify a static scene by setting --scene to a .json file containing data descriptions.

The DTU Scan24 scene can be downloaded from Google Drive:

./build/testbed --scene ${data_path}/transform.json

Or, you can run the experiment in an automated fashion through python bindings:

python scripts/run.py --scene ${data_path}/transform.json --name ${your_experiment_name} --network ${config_name} --n_steps ${training_steps}

For training on DTU dataset, config_name can be dtu.json and training_steps can be 15000.

Also, you can use scripts/run_dynamic.py as:

python scripts/run_dynamic.py --scene ${data_path}/transform.json --name ${your_experiment_name} --network ${config_name}

, where the number of training iterations is specified in the config.

The outputs and logs of the experiment can be found at output/${your_experiment_name}/.

Dynamic Scene

To specify a dynamic scene, you should set --scene to a directory containing .json files that describe training frames.

./build/testbed --scene ${data_dirname}

Or, run scripts/run_dynamic.py using python:

python scripts/run_dynamic.py --scene ${data_dirname} --name ${your_experiment_name} --network ${config_name}

There are some hyperparameters of the network configuration, such as configs/nerf/base.json, to control the dynamic training process:

first_frame_max_training_step: determine the number of training iterations for the first frame, default 2000.
next_frame_max_training_step: determine the number of training iterations for subsequent frames, default 1300, including global transformation prediction.
predict_global_movement: set true if use global transformation prediction.
predict_global_movement_training_step: determine the number of training iterations for global transformation prediction, default 300. Only valid when predict_global_movement is true.

Also, we provide scripts to reconstruct dynamic scenes by reconstructing static scene frame by frame.

python scripts/run_per_frame.py --base_dir ${data_dirname} --output_dir ${output_path} --config ${config_name}

Dynamic scene examples can be downloaded from Google Drive.

Data Convention

Our NeuS2 implementation expects initial camera parameters to be provided in a transforms.json file, organized as follows:

{
	"from_na": true, # specify NeuS2 data format
	"w": 512, # image_width
	"h": 512, # image_height
	"aabb_scale": 1.0,
	"scale": 0.5,
	"offset": [
		0.5,
		0.5,
		0.5
	],
	"frames": [ # list of reference images & corresponding camera parameters
		{
			"file_path": "images/000000.png", # specify the image path (should be relative path)
			"transform_matrix": [ # specify extrinsic parameters of camera, a camera to world transform (shape: [4, 4])
				[
					0.9702627062797546,
					-0.01474287360906601,
					-0.2416049838066101,
					0.9490470290184021
				],
				[
					0.0074799139983952045,
					0.9994929432868958,
					-0.0309509988874197,
					0.052045613527297974
				],
				[
					0.2419387847185135,
					0.028223415836691856,
					0.9698809385299683,
					-2.6711924076080322
				],
				[
					0.0,
					0.0,
					0.0,
					1.0
				]
			],
			"intrinsic_matrix": [ # specify intrinsic parameters of camera (shape: [4, 4])
				[
					2892.330810546875,
					-0.00025863019982352853,
					823.2052612304688,
					0.0
				],
				[
					0.0,
					2883.175537109375,
					619.0709228515625,
					0.0
				],
				[
					0.0,
					0.0,
					1.0,
					0.0
				],
				[
					0.0,
					0.0,
					0.0,
					1.0
				]
			]
		},
		...
	]
}

Each transforms.json file contains data about a single frame, including camera parameters and image paths. You can specify specific transform files, such as transforms_test.json and transforms_train.json, to use for training and testing with data splitting.

For example, you can organize your dynamic scene data as:

<case_name>
|-- images
   |-- 000280 # target frame of the scene
      |-- image_c_000_f_000280.png
      |-- image_c_001_f_000280.png
      ...
   |-- 000281
      |-- image_c_000_f_000281.png
      |-- image_c_001_f_000281.png
      ...
   ...
|-- train
   |-- transform_000280.json
   |-- transform_000281.json
   ...
|-- test
   |-- transform_000280.json
   |-- transform_000281.json
   ...

Images are four-dimensional, with three channels for RGB and one channel for the mask.

We also provide a data conversion from NeuS to our data convention, which can be found in tools/data_format_from_neus.py.

Citation

@inproceedings{neus2,
    title={NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction}, 
    author={Wang, Yiming and Han, Qin and Habermann, Marc and Daniilidis, Kostas and Theobalt, Christian and Liu, Lingjie},
    year={2023},
    booktitle={Proceedings of the IEEE/CVF International Conference on Computer Vision (ICCV)}
}

keyanzhai/NeuS2

NeuS2: Fast Learning of Neural Implicit Surfaces for Multi-view Reconstruction

[Project] [Paper]

Installation

Training

Static Scene

Dynamic Scene

Data Convention

Citation