Point-NeRF: Point-based Neural Radiance Fields

Point-NeRF: Point-based Neural Radiance Fields

Project Sites | Paper | Primary contact: Qiangeng Xu

Point-NeRF uses neural 3D point clouds, with associated neural features, to model a radiance field. Point-NeRF can be rendered efficiently by aggregating neural point features near scene surfaces, in a ray marching-based rendering pipeline. Moreover, Point-NeRF can be initialized via direct inference of a pre-trained deep network to produce a neural point cloud; this point cloud can be finetuned to surpass the visual quality of NeRF with 30X faster training time. Point-NeRF can be combined with other 3D reconstruction methods and handles the errors and outliers in such methods via a novel pruning and growing mechanism.

Reference

Please cite our paper if you are interested
Point-NeRF: Point-based Neural Radiance Fields.    

@article{xu2022point,
  title={Point-NeRF: Point-based Neural Radiance Fields},
  author={Xu, Qiangeng and Xu, Zexiang and Philip, Julien and Bi, Sai and Shu, Zhixin and Sunkavalli, Kalyan and Neumann, Ulrich},
  journal={arXiv preprint arXiv:2201.08845},
  year={2022}
}

Overal Instruction

  1. Please first install the libraries as below and download/prepare the datasets as instructed.
  2. Point Initialization: Download pre-trained MVSNet as below and train the feature extraction from scratch or directly download the pre-trained models. (Obtain 'MVSNet' and 'init' folder in checkpoints folder)
  3. Per-scene Optimization: Download pre-trained models or optimize from scratch as instructed.

We provide all the checkpoint files (google drive) and all the test results images and scores (google drive)

Installation

Requirements

All the codes are tested in the following environment:

  • Linux (tested on Ubuntu 16.04, 18.04, 20.04)
  • Python 3.6+
  • PyTorch 1.7 or higher (tested on PyTorch 1.7, 1.8.1, 1.9, 1.10)
  • CUDA 10.2 or higher

Install

Install the dependent libraries as follows:

  • Install the dependent python libraries:
pip install torch==1.8.1+cu102 h5py
pip install imageio scikit-image

We develope our code with pytorch1.8.1 and pycuda2021.1

Data Preparation

The layout should looks like this:

pointnerf
├── data_src
│   ├── dtu
    │   │   │──Cameras
    │   │   │──Depths
    │   │   │──Depths_raw
    │   │   │──Rectified
    ├── nerf
    │   │   │──nerf_synthetic
    ├── nsvf
    │   │   │──Synthetic_NeRF
    ├── scannet
    │   │   │──scans 
    |   │   │   │──scene0101_04
    |   │   │   │──scene0241_01

DTU:

Download the preprocessed DTU training data and Depth_raw from original MVSNet repo and unzip.

NeRF Synthetic

Download nerf_synthetic.zip from here under ``data_src/nerf/''

Tanks & Temples

Follow Neural Sparse Voxel Fields and download Tanks&Temples | download (.zip) | 0_* (training) 1_* (testing) under: ``data_src/nsvf/''

ScanNet

Download and extract ScanNet by following the instructions provided at http://www.scan-net.org/. The detailed steps including:

  • Go to http://www.scan-net.org and fill & sent the request form.
  • You will get a email that has command instruction and a download-scannet.py file, this file is for python 2, you can use our download-scannet.py in the ``data'' directory for python 3.
  • clone the official repo:
    git clone https://github.com/ScanNet/ScanNet.git
    
  • Download specific scenes (used by NSVF):
     python data/download-scannet.py -o ../data_src/scannet/ id scene0101_04 
     python data/download-scannet.py -o ../data_src/scannet/ id scene0241_01
    
  • Process the sens files:
      python ScanNet/SensReader/python/reader.py --filename data_src/nrData/scannet/scans/scene0101_04/scene0101_04.sens  --output_path data_src/nrData/scannet/scans/scene0101_04/exported/ --export_depth_images --export_color_images --export_poses --export_intrinsics
      
      python ScanNet/SensReader/python/reader.py --filename data_src/nrData/scannet/scans/scene0241_01/scene0241_01.sens  --output_path data_src/nrData/scannet/scans/scene0241_01/exported/ --export_depth_images --export_color_images --export_poses --export_intrinsics
    

Point Initialization / Generalization:

  Download pre-trained MVSNet checkpoints:

We trained MVSNet on DTU. You can Download ''MVSNet'' directory from google drive and place them under '''checkpoints/'''

  Train 2D feature extraction and point representation

  Directly use our trained checkpoints files:

Download ''init'' directory from google drive. and place them under '''checkpoints/'''

  Or train from scratch:

Train for point features of 63 channels (as in paper)

bash dev_scripts/ete/dtu_dgt_d012_img0123_conf_color_dir_agg2.sh

Train for point features of 32 channels (better for per-scene optimization)

bash dev_scripts/ete/dtu_dgt_d012_img0123_conf_agg2_32_dirclr20.sh

After the training, you should pick a checkpoint and rename it to best checkpoint, e.g.:

cp checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/250000_net_ray_marching.pth  checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/best_net_ray_marching.pth

cp checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/250000_net_mvs.pth  checkpoints/dtu_dgt_d012_img0123_conf_color_dir_agg2/best_net_mvs.pth

  Test feed forward inference on dtu scenes

These scenes that are selected by MVSNeRF, please also refer their code to understand the metrics calculation.

bash dev_scripts/dtu_test_inf/inftest_scan1.sh
bash dev_scripts/dtu_test_inf/inftest_scan8.sh
bash dev_scripts/dtu_test_inf/inftest_scan21.sh
bash dev_scripts/dtu_test_inf/inftest_scan103.sh
bash dev_scripts/dtu_test_inf/inftest_scan114.sh

Per-scene Optimization:

(Please visit the project sites to see the original videos of above scenes, which have quality loss when being converted to gif files here.)

Download per-scene optimized Point-NeRFs

You can skip training and download the folders of ''nerfsynth'', ''tanksntemples'' and ''scannet'' here google drive, and place them in ''checkpoints/''.

pointnerf
├── checkpoints
│   ├── init
    ├── MVSNet
    ├── nerfsynth
    ├── scannet
    ├── tanksntemples

In each scene, we provide initialized point features and network weights ''0_net_ray_marching.pth'', points and weights at 20K steps ''20000_net_ray_marching.pth'' and 200K steps ''200000_net_ray_marching.pth''

Test the per-scene optimized Point-NeRFs

NeRF Synthetics

test scripts
    bash dev_scripts/w_n360/chair_test.sh
    bash dev_scripts/w_n360/drums_test.sh
    bash dev_scripts/w_n360/ficus_test.sh
    bash dev_scripts/w_n360/hotdog_test.sh
    bash dev_scripts/w_n360/lego_test.sh
    bash dev_scripts/w_n360/materials_test.sh
    bash dev_scripts/w_n360/mic_test.sh
    bash dev_scripts/w_n360/ship_test.sh

ScanNet

test scripts
    bash dev_scripts/w_scannet_etf/scane101_test.sh
    bash dev_scripts/w_scannet_etf/scane241_test.sh

Tanks & Temples

test scripts
    bash dev_scripts/w_tt_ft/barn_test.sh
    bash dev_scripts/w_tt_ft/caterpillar_test.sh
    bash dev_scripts/w_tt_ft/family_test.sh
    bash dev_scripts/w_tt_ft/ignatius_test.sh
    bash dev_scripts/w_tt_ft/truck_test.sh

Per-scene optimize from scatch

Make sure the ''checkpoints'' folder has ''init'' and ''MVSNet''. The training scripts will start to do initialization if there is no ''.pth'' files in a scene folder. It will start from the last ''.pth'' files until reach the iteration of ''maximum_step''.

NeRF Synthetics

train scripts
    bash dev_scripts/w_n360/chair.sh
    bash dev_scripts/w_n360/drums.sh
    bash dev_scripts/w_n360/ficus.sh
    bash dev_scripts/w_n360/hotdog.sh
    bash dev_scripts/w_n360/lego.sh
    bash dev_scripts/w_n360/materials.sh
    bash dev_scripts/w_n360/mic.sh
    bash dev_scripts/w_n360/ship.sh

ScanNet

train scripts
    bash dev_scripts/w_scannet_etf/scane101.sh
    bash dev_scripts/w_scannet_etf/scane241.sh

Tanks & Temples

train scripts
    bash dev_scripts/w_tt_ft/barn.sh
    bash dev_scripts/w_tt_ft/caterpillar.sh
    bash dev_scripts/w_tt_ft/family.sh
    bash dev_scripts/w_tt_ft/ignatius.sh
    bash dev_scripts/w_tt_ft/truck.sh

Acknowledgement

Our repo is developed based on MVSNet, NeRF, MVSNeRF, and NSVF.

Please also consider citing the corresponding papers.

The project is conducted collaboratively between Adobe Research and University of Southern California.

LICENSE

The repo is licensed under Creative Commons Attribution-NonCommercial-ShareAlike 2.0, and is restricted to academic use only. See LICENSE.

Comments
  • FileNotFoundError of pointcloud

    FileNotFoundError of pointcloud

    Hi, thanks for your great job! I have the same problem when I run "bash dev_scripts/w_n360/ship_test.sh". It goes with "FileNotFoundError: [Errno 2] No such file or directory" in line 118 of load_blender.py. My datapath is pointnerf/data_src/nerf/nerf_synthetic/ship,and it includes 3 .json files and 3 folders containing some .png pictures. At the same time, the checkpoints folder only include some .pth files. It doesn't seem to contain the saved point cloud.

    Could you please tell me where the "point_path" is? Thank you~

  • loss item

    loss item

    Thanks for sharing this work. i am a little confused about the loss item. i know that 'coarse_raycolor' is corresponding to L_render in paper, what is 'ray_masked_loss' and 'ray_miss_loss'? And if i understand correctly, 'zero_one_loss_items' is the L_sparse in paper. but since we already set the neural points as input data and didn't load mvsnet model, can the parameters of mvsnet also be updated? (when i inspect the variable , the only model is "ray_marching") And you set the neural points to nn.parameter to update, any reasons behind it?

  • Arguments are required --name

    Arguments are required --name

    Hi everyone, I have a problem when launching bash scripts like "bash dev_scripts/w_n360/chair.sh", the argument 'name' is not detected and the terminal returns me the error: train_ft.py: error: the following arguments are required: --name Did any of you had the same problem?

  • Why is it slow for Per-scene Optimization?

    Why is it slow for Per-scene Optimization?

    I run this command to do Per-scene optimize from scratch: bash dev_scripts/w_n360/lego.sh

    In the paper, the speed should be 2min / 1K iters. However, on my single 2080Ti, it takes 100min / 1K iters. Anyone meets this problem?

  • "No such file or directory" only for scannet scene

    Hi , when I redownload the code repo and run from scratch ,It still encounters with the problem of "No such file or directory" when I run scene101.sh ,However it works fine if I run from scratch in the dataset of Nerf Synth. Is there some possiblity that I should change some setting for the scannet scene. https://github.com/Xharlie/pointnerf/issues/7#issuecomment-1060343926

  • Loss

    Loss

    In base_rendering_model, loss = self.l2loss(masked_output, masked_gt) * masked_gt.shape[1], why multiply masked_gt.shape[1]? https://github.com/Xharlie/pointnerf/blob/a614e1d15cc8409e14f80c7b3dd0091939fef758/models/base_rendering_model.py#L560

  • How to use point cloud as input

    How to use point cloud as input

    Hello authors, thank you very much for your paper and for releasing code! I am very excited about this work. I have gotten point cloud data in a ply format somehow, how do I use this data as input to point_nerf. I don't seem to find it in "READEME.md". image

  • pycuda.driver.CompileError

    pycuda.driver.CompileError

    Hello, Thanks for the awesome work. However when I run the Per-scene Optimization on NeRF-Synthetics I found some error below:

    ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Debug Mode ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ /home/zhaoboming/anaconda3/envs/nerf-w/lib/python3.7/site-packages/numpy/core/shape_base.py:420: VisibleDeprecationWarning: Creating an ndarray from ragged nested sequences (which is a list-or-tuple of lists-or-tuples-or ndarrays with different lengths or shapes) is deprecated. If you meant to do this, you must specify 'dtype=object' when creating the ndarray. arrays = [asanyarray(arr) for arr in arrays] /home/zhaoboming/anaconda3/envs/nerf-w/lib/python3.7/site-packages/torch/functional.py:568: UserWarning: torch.meshgrid: in an upcoming release, it will be required to pass the indexing argument. (Triggered internally at /opt/conda/conda-bld/pytorch_1646755953518/work/aten/src/ATen/native/TensorShape.cpp:2228.) return _VF.meshgrid(tensors, **kwargs) # type: ignore[attr-defined] dataset total: train 100 dataset [NerfSynthFtDataset] was created ../checkpoints/nerfsynth/lego/*_net_ray_marching.pth ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ Continue training from 200000 epoch Iter: 200000 ++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++++ opt.act_type!!!!!!!!! LeakyReLU self.points_embeding torch.Size([1, 479862, 32]) querier device cuda:3 3 Traceback (most recent call last): File "train_ft.py", line 1084, in main() File "train_ft.py", line 637, in main model = create_model(opt) File "/mnt/data1/zhaoboming/pointnerf/run/../models/init.py", line 39, in create_model instance.initialize(opt) File "/mnt/data1/zhaoboming/pointnerf/run/../models/base_rendering_model.py", line 369, in initialize self.create_network_models(opt) File "/mnt/data1/zhaoboming/pointnerf/run/../models/mvs_points_volumetric_model.py", line 44, in create_network_models super(MvsPointsVolumetricModel, self).create_network_models(opt) File "/mnt/data1/zhaoboming/pointnerf/run/../models/neural_points_volumetric_model.py", line 157, in create_network_models params = self.get_additional_network_params(opt) File "/mnt/data1/zhaoboming/pointnerf/run/../models/neural_points_volumetric_model.py", line 142, in get_additional_network_params self.neural_points = NeuralPoints(opt.point_features_dim, opt.num_point, opt, self.device, checkpoint=checkpoint_path, feature_init_method=opt.feature_init_method, reg_weight=0., feedforward=opt.feedforward) File "/mnt/data1/zhaoboming/pointnerf/run/../models/neural_points/neural_points.py", line 331, in init self.querier = self.lighting_fast_querier(device, self.opt) File "/mnt/data1/zhaoboming/pointnerf/run/../models/neural_points/query_point_indices_worldcoords.py", line 39, in init self.claim_occ, self.map_coor2occ, self.fill_occ2pnts, self.mask_raypos, self.get_shadingloc, self.query_along_ray = self.build_cuda() File "/mnt/data1/zhaoboming/pointnerf/run/../models/neural_points/query_point_indices_worldcoords.py", line 524, in build_cuda """, no_extern_c=True) File "/home/zhaoboming/anaconda3/envs/nerf-w/lib/python3.7/site-packages/pycuda-2021.1-py3.7-linux-x86_64.egg/pycuda/compiler.py", line 358, in init include_dirs, File "/home/zhaoboming/anaconda3/envs/nerf-w/lib/python3.7/site-packages/pycuda-2021.1-py3.7-linux-x86_64.egg/pycuda/compiler.py", line 298, in compile return compile_plain(source, options, keep, nvcc, cache_dir, target) File "/home/zhaoboming/anaconda3/envs/nerf-w/lib/python3.7/site-packages/pycuda-2021.1-py3.7-linux-x86_64.egg/pycuda/compiler.py", line 87, in compile_plain checksum.update(preprocess_source(source, options, nvcc).encode("utf-8")) File "/home/zhaoboming/anaconda3/envs/nerf-w/lib/python3.7/site-packages/pycuda-2021.1-py3.7-linux-x86_64.egg/pycuda/compiler.py", line 59, in preprocess_source "nvcc preprocessing of %s failed" % source_path, cmdline, stderr=stderr pycuda.driver.CompileError: nvcc preprocessing of /tmp/tmpeytiys5x.cu failed [command: nvcc --preprocess -arch sm_86 -I/mnt/data1/zhaoboming/anaconda3/envs/nerf-w/lib/python3.7/site-packages/pycuda-2021.1-py3.7-linux-x86_64.egg/pycuda/cuda /tmp/tmpeytiys5x.cu --compiler-options -P] [stderr: b'cc1plus: fatal error: cuda_runtime.h: No such file or directory\ncompilation terminated.\n'] end loading


    PyCUDA ERROR: The context stack was not empty upon module cleanup.

    A context was still active when the context stack was being cleaned up. At this point in our execution, CUDA may already have been deinitialized, so there is no way we can finish cleanly. The program will be aborted now. Use Context.pop() to avoid this problem.

    So How can I fix it? Thanks.

  • DTU dataset download

    DTU dataset download

    Hello,

    Thanks for the awesome work. However, I am not able to download the DTU training dataset from the gdrive link shared in the repo. It seems that the link is dead. Would it be possible for you to reactivate the link?

    Thanks, Aditya Vora

  • Any instruction for our own data preparation?

    Any instruction for our own data preparation?

    Hi there, I followed all of your instructions and now can train from the scratch. However, I met some difficulties when trying point nerf on my own data. I think some more instructions may be needed for data preparation. Thanks a lot!

  •  what prempl does?

    what prempl does?

    Thanks for your great work! I would like to know what premlp(https://github.com/Xharlie/pointnerf/blob/master/models/mvs/mvs_points_model.py line22) does, just turn 63-dimensional input into 32-dimensional? How is the weight of this part set?

  • Error on gen_points_filter_embeddings()

    Error on gen_points_filter_embeddings()

    Hello authors, thank you very much for your paper and for releasing code!

    I am trying to run : bash ./dev_scripts/w_n360/ship.sh

    When I run this script, I get the follow log and error:

    -----------------------------------Generate Points----------------------------------- model [MvsPointsVolumetricModel] was created opt.resume_iter!!!!!!!!! best loading mvs from ../checkpoints/init/dtu_dgt_d012_img0123_conf_agg2_32_dirclr20/best_net_mvs.pth cannot load ../checkpoints/init/dtu_dgt_d012_img0123_conf_agg2_32_dirclr20/best_net_mvs.pth ------------------- Networks ------------------- [Network mvs] Total number of parameters: 0.382M

    0%| | 0/543 [00:00<?, ?it/s]/usr/local/lib/python3.7/dist-packages/torch/nn/functional.py:3829: UserWarning: Default grid_sample and affine_grid behavior has changed to align_corners=False since 1.3.0. Please specify align_corners=True if the old behavior is desired. See the documentation of grid_sample for details. "Default grid_sample and affine_grid behavior has changed " 100%| 543/543 [02:26<00:00, 3.71it/s] 100%| 543/543 [31:52<00:00, 3.52s/it] xyz_world_all torch.Size([0, 3]) torch.Size([0, 1]) torch.Size([0]) %%%%%%%%%%%%% getattr(dataset, spacemin, None) None vishull_mask torch.Size([0]) alpha masking xyz_world_all torch.Size([0, 3]) torch.Size([0, 1]) Traceback (most recent call last): File "train_ft.py", line 1084, in main() File "train_ft.py", line 639, in main points_xyz_all, points_embedding_all, points_color_all, points_dir_all, points_conf_all, img_lst, c2ws_lst, w2cs_lst, intrinsics_all, HDWD_lst = gen_points_filter_embeddings(train_dataset, visualizer, opt) File "train_ft.py", line 142, in gen_points_filter_embeddings xyz_world_all, sparse_grid_idx, sampled_pnt_idx = mvs_utils.construct_vox_points_closest(xyz_world_all.cuda() if len(xyz_world_all) < 99999999 else xyz_world_all[::(len(xyz_world_all)//99999999+1),...].cuda(), opt.vox_res) File "/root/pointnerf/run/../models/mvs/mvs_utils.py", line 541, in construct_vox_points_closest xyz_min, xyz_max = torch.min(xyz, dim=-2)[0], torch.max(xyz, dim=-2)[0] RuntimeError: cannot perform reduction function min on tensor with no elements because the operation does not have an identity end loading

    I add some log to debug this issue as follow , found out: all item of confidence_all equals to 0.0208.

    torch.set_printoptions(profile="full") print(cam_xyz_all) _, xyz_world_all, confidence_filtered_all = filter_utils.filter_by_masks_gpu(cam_xyz_all, intrinsics_all, extrinsics_all, confidence_all, points_mask_all, opt, vis=True, return_w=True, cpu2gpu=cpu2gpu, near_fars_all=near_fars_all)

  • Do you evaluate your method on a mixture of training and testing images of ScanNet?

    Do you evaluate your method on a mixture of training and testing images of ScanNet?

    I am confused about the training and testing split. Please see https://github.com/Xharlie/pointnerf/blob/937709436a4b310b046c4746edc016361a1cf6d4/data/scannet_ft_dataset.py#L302

    In the above code, when self.opt.test_num_step is 1, the whole self.all_id_list is returned as testing list. Since self.all_id_list contains all the frame id of the dataset, the codes evaluate methods on all frames of a scene, including the training frames.

  • PyCUDA ERROR: The context stack was not empty upon module cleanup

    PyCUDA ERROR: The context stack was not empty upon module cleanup

    After running ‘bash dev_scripts/w_scannet_etf/scane241_test.sh’ and 'bash dev_scripts/w_n360/chair_test.sh' I got this PyCUDA error: image Does anybody know how to solve it?

  • About

    About

    Thanks for sharing the code! PointNerf's synthesis result is really amazing!

    I've read the code and confused about how to compute the ray_dist. In vanilla NeRF, the sigma is usually computed using ray-distance, but PointNerf seems using depth instead of ray distance to compute sigma? https://github.com/Xharlie/pointnerf/blob/937709436a4b310b046c4746edc016361a1cf6d4/models/neural_points_volumetric_model.py#L271

  • About points color

    About points color

    Hi, Thanks for the awesome work. However I have a doubt about the parameters Neural_points.points_color, why they are negative? So what does this 'color' represent?

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