generateViews

Generate novel views using Nerfacto Neural Radiance Field (NeRF) model

Since R2026a

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    Syntax

    I = generateViews(nerf,cameraPoses)
    I = generateViews(___,Intrinsics=virtualCameraIntrinsics)

    Description

    Add-On Required: This feature requires the Computer Vision Toolbox Interface for Nerfstudio Library add-on.

    I = generateViews(nerf,cameraPoses) generates novel views using the trained Nerfacto NeRF model [1] from the Nerfstudio library [2], for the specified virtual camera poses cameraPoses.

    Note

    This feature requires the Computer Vision Toolbox Interface for Nerfstudio Library add-on, a Deep Learning Toolbox™ license, a Parallel Computing Toolbox™ license, and a CUDA® enabled NVIDIA® GPU with at least 16 GB of available GPU memory.

    example

    I = generateViews(___,Intrinsics=virtualCameraIntrinsics) specifies the intrinsic parameters of the virtual camera in addition to all input arguments from the previous syntax.

    Examples

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    This example uses:

    Open Live Script

    Download Pretrained NeRF Model

    The tum_rgbd_nerfacto.zip file contains the trainedNerfactoTUMRGBD folder. The folder contains the saved-nerfacto-tum-rgbd.mat supporting file, which contains a nerfacto object that has been trained on a sequence of indoor images from the TUM RGB-D data set [1], and the nerfactoModelFolder folder, which contains the data and configuration files required for the Nerfstudio Library [2] to load and execute the trained NeRF model [3].

    Download and extract tum_rgbd_nerfacto.zip.

    if ~exist("tum_rgbd_nerfacto.zip","file")
    websave("tum_rgbd_nerfacto.zip","https://ssd.mathworks.com/supportfiles/3DReconstruction/tum_rgbd_nerfacto.zip");
    unzip("tum_rgbd_nerfacto.zip", pwd);
end

    Specify the path to the folder containing the saved-nerfacto-tum-rgbd MAT file, and then load the pretrained nerfacto object into the workspace.

    modelRoot = fullfile(pwd, "trainedNerfactoTUMRGBD");
load(fullfile(modelRoot, "saved-nerfacto-tum-rgbd.mat"))
    Warning: The model folder for the nerfacto object could not be loaded. Update the model folder location using the <a href="matlab:doc('changePath')">changePath</a> method.
  '\\mathworks\devel\sandbox\user\Shared\EX_25b\EX_nerfacto_feature\train_nerfacto_small'

    MATLAB® displays a warning when you first load the pretrained nerfacto object into the workspace because the path to the model folder associated with the object, nerfactoModelFolder, changes when you extract the nerfacto_saved_model.zip.

    To resolve this issue, use the changePath function to update the ModelFolder property of the trained nerfacto object to the current path of nerfactoModelFolder. This process can take a few minutes.

    nerf = changePath(nerf, fullfile(modelRoot, "nerfactoModelFolder"));
    Changing nerfacto object model folder path.
nerfacto object model folder path change complete.

    Display the nerfacto object properties to verify that the ModelFolder property is set to the current path of nerfactoModelFolder on your system.

    disp(nerf)
      nerfacto with properties:

      ModelFolder: "/home/user/Documents/MATLAB/ExampleManager/user.Bdoc.EX_26a_v1/vision-ex39493372/trainedNerfactoTUMRGBD/nerfactoModelFolder"
    MaxIterations: 30000
       Intrinsics: [1×1 cameraIntrinsics]

    Load Camera Poses

    This example contains a set of predefined camera poses that are stored as an array of rigidtform3d objects in the camera-poses-tum.mat file. These represent the position and orientation of the camera in the 3-D world coordinate system of the scene.

    Load the camera-poses-tum.mat file into workspace, which contains the camera poses camPoses.

    load("camera-poses-tum.mat","camPoses");

    Display the camera poses by using the plotCamera function.

    figure
pcshow([NaN NaN NaN], VerticalAxis="y", VerticalAxisDir="down");
xlabel("X")
ylabel("Y")
zlabel("Z")
hold on

colors = sky(length(camPoses));
for i = 1:length(camPoses)
plotCamera(camPoses(i), Size=0.1, Color=colors(i,:), Opacity=0.1);
end

hold off

    Figure contains an axes object. The axes object with xlabel X, ylabel Y contains 51 objects of type line, text, patch, scatter.

    Generate Novel Views with Pretrained NeRF Model

    Generate the views captured by the camera poses camPoses by using the generateViews function.

    images = generateViews(nerf,camPoses);
    Generating views from nerfacto object.
Loading latest checkpoint from load_dir
nerfacto object view generation complete.

    Tip: If you want to generate a large number of novel views, use the writeViews function instead of generateViews to avoid an out-of-memory error.

    Display the generated images. The images show the globe and chairs in front of the desk that are present in the images used to train this NeRF model.

    figure
montage(images, BorderSize=[2 2])
title("Rendered Images from Trained NeRF")

    Figure contains an axes object. The hidden axes object with title Rendered Images from Trained NeRF contains an object of type image.

    References

    [1] Sturm, Jürgen, Nikolas Engelhard, Felix Endres, Wolfram Burgard, and Daniel Cremers. “A Benchmark for the Evaluation of RGB-D SLAM Systems.” 2012 IEEE/RSJ International Conference on Intelligent Robots and Systems, October 2012, 573–80. https://doi.org/10.1109/IROS.2012.6385773.

    [2] Tancik, Matthew, Ethan Weber, Evonne Ng, et al. “Nerfstudio: A Modular Framework for Neural Radiance Field Development.” Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings, ACM, July 23, 2023, 1–12. https://doi.org/10.1145/3588432.3591516.

    [3] Mildenhall, Ben, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. "NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis." In Computer Vision - ECCV 2020, edited by Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm. Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-58452-8_24.

    Input Arguments

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    Trained Nerfacto NeRF model, specified as a nerfacto object.

    Virtual camera poses, specified as an M-by-1 vector of rigidtform3d objects, where M is the number novel views that you want to generate. Each element of the vector specifies a virtual camera pose for a novel view that you want to generate in the world coordinate system. You must specify the virtual camera poses in the same world coordinate system as the camera poses of the training images.

    Tip

    Generating a large number of views can cause an out of memory error because the generateViews function stores the generated images in memory. To generate a large number of views, use the writeViews function, which stores the generated images as files, instead.

    Virtual camera intrinsic parameters, specified as a cameraIntrinsics object. This function supports the intrinsic parameters of a pinhole camera model, which consist of focal length, principal point, and skew, and ignores other parameter values such as lens distortion.

    By default, the generateViews function generates novel views using a virtual camera with the same intrinsic parameter as the camera that captures the training images. However, you can specify a virtual camera with intrinsic parameters that are different from the camera intrinsic parameters of the training images, such as to generate novel views with a higher resolution or wider field-of-view compared to the training images.

    Output Arguments

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    Novel views, returned as an H-by-W-by-3-by-M array, where M is the number of novel views. Each image is of size H-by-W-by-3, where H and W are the height and width of the image, in pixels.

    References

    [1] Mildenhall, Ben, Pratul P. Srinivasan, Matthew Tancik, Jonathan T. Barron, Ravi Ramamoorthi, and Ren Ng. “NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis.” In Computer Vision – ECCV 2020, edited by Andrea Vedaldi, Horst Bischof, Thomas Brox, and Jan-Michael Frahm. Springer International Publishing, 2020. https://doi.org/10.1007/978-3-030-58452-8_24.

    [2] Tancik, Matthew, Ethan Weber, Evonne Ng, et al. “Nerfstudio: A Modular Framework for Neural Radiance Field Development.” Special Interest Group on Computer Graphics and Interactive Techniques Conference Proceedings, ACM, July 23, 2023, 1–12. https://doi.org/10.1145/3588432.3591516.

    Version History

    Introduced in R2026a

    See Also

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