NeISF: Neural Incident Stokes Field for Geometry and Material Estimation

Chenhao Li^1,3, Taishi Ono², Takeshi Uemori¹, Hajime Mihara¹, Alexander Gatto², Hajime Nagahara³, and Yusuke Moriuchi¹

¹ Sony Semiconductor Solutions Corporation, ² Sony Europe B.V., ³ Osaka University

This project is the implementation of "NeISF: Neural Incident Stokes Field for Geometry and Material Estimation (CVPR 2024)", which is a novel multi-view inverse rendering framework that reduces ambiguities using polarization cues.

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Table of Contents

• Dependencies
• Folder structure
• Preparation
• Run
  • Training
  • Testing
  • Evaluation
  • Exporting 3D mesh from trained SDFs
  • Re-lighting
  • Exporting UV textures and Blender rendered animation
• Dataset
  • Polarized images
  • Masks
  • Camera poses
  • Camera normalization
• Config files
  • Common parameters
  • TrainerNeISF
• License
• Citation

Dependencies

• Python 3.10 or newer (tested on 3.10.4 and 3.10.8).
• CUDA 10.1 or newer (tested on 11.3 and 10.1).

For the other dependencies, please see requirements.txt.

Folder structure

Our scripts assume the following folder structure and file names. See also images/sample_folder.

|- train.py
|- inference.py
|- ...
|- mymodules/
|- configs/
|- configs_sample/
|- results/
|- images/
    |- folder_1/
        |- poses_bounds.npy
        |- images_s0/
            |- img_001.exr  # please follow this naming convention.
            |- img_002.exr
            |- ...
        |- images_s1/
            |- img_001.exr
            |- img_002.exr
            |- ...
        |- images_s2/
            |- img_001.exr
            |- img_002.exr
            |- ...
        |- masks/
            |- img_001.png  # 16-bit, 3 channels.
            |- img_002.png
            |- ...

Preparation

Install all the dependency using requirements.txt, or, if you are a Docker user, you can use Dockerfile.

Copy all the config files from configs_sample/ to configs/.

Run

Training

This project includes three trainers: TrainverVolSDF, TrainerNeISF, and TrainerNeISFNoStokes.

For example, if you want to use TrainverVolSDF:

1. Edit configs/trainervolsdf_config.json.
2. Run the following command:

   $ python train.py trainervolsdf_config.json
   

As described in the paper (Sec. 4.6), the full pipeline of NeISF is composed of the following three steps:

1. train TrainverVolSDF using trainervolsdf_config.json.
2. train TrainerNeISF using trainer_neisf_init_config.json.
3. train TrainerNeISF using trainer_neisf_joint_config.json.

To reproduce our results, run these three trainings with using the written parameters.

If you want to train TrainerNeISFNoStokes, please do the step 2 and 3 with using trainer_neisfnostokes_init_config.json and trainer_neisfnostokes_joint_config.json, respectively.

Testing

$ python inference.py {RESULT FOLDER NAME} {IMAGE FOLDER NAME} {EPOCH NUM} -b {BATCH SIZE}

Evaluation

$ python compute_metrics.py {RESULT FOLDER NAME} {IMAGE FOLDER NAME} {EPOCH NUM} -l {IMAGE_DOMAIN1} {IMAGE_DOMAIN2} ...

About the args or metrics, more details can be found in compute_metrics.py.

Exporting 3D mesh from trained SDFs

Run the following command:

$ python generate_mesh_from_sdf.py {RESULT FOLDER NAME} {EPOCH_NUM} {resolution}

Re-lighting

1. Locate your environment illumination map (must be EXR format) under env_maps.
2. Run the following command:

$ python generate_relighting_image.py {YOUR RESULT FOLDER} {TARGET FOLDER} {EPOCH NUM} {ENV MAP NAME} -b {B SIZE} -l {SAMPLE ILLUM NUM}

Exporting UV textures and Blender rendered animation

1. Locate your environment illumination map (must be EXR format) under env_maps.
2. Run the following command:

$ python generate_3d_blender_data.py {YOUR RESULT FOLDER} {EPOCH NUM} {MESH RESOLUTION} {ENV MAP NAME}

Known issues: We have observed some environments where this script can not correctly render videos. In this case, you may use Dockerfile.

Dataset

Here we describe how to prepare your own dataset.

Polarized images

Please see our appendix about how we created our HDR polarized dataset. In the same way as the convention, s0, s1, and s2 images representing the Stokes vectors are defined as follows:

• s0 = (i_000 + i_045 + i_090 + i_135) / 2.
• s1 = i_000 - i_090
• s2 = i_045 - i_135

Save the images according to the Folder structure. Please also check sample_folder.

Masks

Our method requires binary masks as inputs, the masks must follow the following rules:

• 16bit png with three channels.
• maximum intensity (white) represents valid pixels, minimum intensity (black) represents invalid ones.
• The same resolution as the polarized images.

Please also check sample_folder.

Camera poses

We use the same format as LLFF to describe the camera extrinsic and intrinsic parameters. poses_bounds.npy stores a numpy array of size Nx17 (where N is the number of input images).

Assuming we have the following world-to-camera matrix (R), camera position (t), and camera intrinsics (h, w, f):

R = [[r00, r01, r02], [r10, r11, r12], [r20, r21, r22]]
t = [t0, t1, t2]
height = h, width = w, focal length = f

then, one of the rows in poses_bounds.npy becomes like:

poses_bounds[i, :] = [r00, r01, r02, t0, h, r10, r11, r12, t1, w, r20, r21, r22, t2, f, 0, 0]

• The last two elements, which are used in LLFF to compute the near and far bound, are not used in this project.
• [x,y,z] axes of the camera point [down, right, backwards].

Camera normalization

Our scripts assume the following conditions:

• All the camera positions are located inside the sphere of radius=3.
• All the cameras are not looking inside out.

To assure the first assumption, please normalize your cameras by using the following command:

$ python preprocess_camera_normalization.py --flist {YOUR_DATA_DIR1} {YOUR_DATA_DIR2} {YOUR_DATA_DIR3} ...

This script will compute the viewing point by using the z-axes of all the cameras, shift the point to the origin, and then normalize all the cameras. If you want to normalize several directories at the same time, for instance you have a training scene and an evaluation scene, please input multiple directories as in the example above.

For visualizing your cameras, use the following command:

$ python visualize_cameras.py {YOUR_DATA_DIR} {DATASET_TYPE}

Currently, only neisf is allowed for DATASET_TYPE.

Config files

Here we describe the parameters included in the config files.

Common parameters

parameter name	definition
trainer_name	the name of the trainer
data_dir	the name of the directory for training
dataset_type	the type of the dataset. the current implementation only accepts neisf.
experiment_name	the name of your result folder.
batch_size	the number of sampled pixels in one iteration.
max_epoch	the maximum number of epoch.
sample_num	the number of 3D points sampled along one ray.
positional_encoding_x_res	the dimension of positional encoding fot the 3D position x.
positional_encoding_d_res	the dimension of positional encoding fot ray direction d.
gpu_num	the number of GPUs (multi-GPU is not supported).
lr	learning rate.
weights	a dictionary to store all the weight values. eik_weight is necessary.
use_mask	if true, invalid pixels are not sampled (false is not well tested).

TrainerNeISF

parameter name	definition
previous_stage_dir	experiment name of the previous stage
previous_stage_epoch_num	which epoch should be loaded
stage_name	name of the current training stage: init or joint
max_step_ray_march	the number of ray-marching steps

License

This software is released under the MIT License. See LICENSE for details.

Citation

@InProceedings{Li_NeISF_CVPR2024,
    author    = {Li, Chenhao and Ono, Taishi and Uemori, Takeshi and Mihara, Hajime and Gatto, Alexander and Nagahara, Hajime and Moriuchi, Yusuke},
    title     = {NeISF: Neural Incident Stokes Field for Geometry and Material Estimation},
    booktitle = {Proceedings of the IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)},
    month     = {June},
    year      = {2024},
    pages     = {21434-21445}
}