The shiny folder contains 8 scenes with challenging view-dependent effects used in our paper. We also provide additional scenes in the shiny_extended folder. The test images for each scene used in our paper consist of one of every eight images in alphabetical order.

Each scene contains the following directory structure:

  scene/
    dense/
      cameras.bin
      images.bin
      points3D.bin
      project.ini
    images/
      image_name1.png
      image_name2.png
      ...
      image_nameN.png
    images_distort/
      image_name1.png
      image_name2.png
      ...
      image_nameN.png
    sparse/
      cameras.bin
      images.bin
      points3D.bin
      project.ini
    database.db
    hwf_cxcy.npy
    planes.txt
    poses_bounds.npy

• dense/ folder contains COLMAP's output [1] after the input images are undistorted.
• images/ folder contains undistorted images. (We use these images in our experiments.)
• images_distort/ folder contains raw images taken from a smartphone.
• sparse/ folder contains COLMAP's sparse reconstruction output [1].

Our poses_bounds.npy is similar to the LLFF[2] file format with a slight modification. This file stores a Nx14 numpy array, where N is the number of cameras. Each row in this array is split into two parts of sizes 12 and 2. The first part, when reshaped into 3x4, represents the camera extrinsic (camera-to-world transformation), and the second part with two dimensions stores the distances from that point of view to the first and last planes (near, far). These distances are computed automatically based on the scene’s statistics using LLFF’s code. (For details on how these are computed, see this code)

hwf_cxcy.npy stores the camera intrinsic (height, width, focal length, principal point x, principal point y) in a 1x5 numpy array.

planes.txt stores information about the MPI planes. The first two numbers are the distances from a reference camera to the first and last planes (near, far). The third number tells whether the planes are placed equidistantly in the depth space (0) or inverse depth space (1). The last number is the padding size in pixels on all four sides of each of the MPI planes. I.e., the total dimension of each plane is (H + 2 * padding, W + 2 * padding).

References:

• [1]: COLMAP structure from motion (Schönberger and Frahm, 2016).
• [2]: Local Light Field Fusion: Practical View Synthesis with Prescriptive Sampling Guidelines (Mildenhall et al., 2019).