is it necessary to load everything in memory?

[Ran Cheng]

The whole dataset was loaded to memory for further computation, is this necessary for the upcoming nerf render?

    # Read images and depth maps for which valid poses exist
    for i in train_frame_ids:
        if valid_poses[i]:
            img = imageio.imread(os.path.join(basedir, 'images', img_files[i]))
            depth = imageio.imread(os.path.join(basedir, 'depth_filtered', depth_files[i]))

            images.append(img)
            depth_maps.append(depth)
            poses.append(all_poses[i])
            frame_indices.append(i)

People usually don't have very large memory size if you are loading everything in memory, could you please consider shift to dataloader like in pytorch?

[Dejan]

In an early version of my method, I observed better results when optimizing with rays randomly selected over the entire corpus of input images instead of using rays from a single image in each batch. I haven't tested if that's still the case with the full method, but I'd assume it is. There is probably a way to implement random ray selection efficiently without pre-loading everything into memory and shuffling, but at the moment I have no plans of doing that.

[Ran Cheng]

thanks for your reply!