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Radiance field renderer
Summary: Implements the main NeRF model class that controls the radiance field and its renderer Reviewed By: nikhilaravi Differential Revision: D25684419 fbshipit-source-id: fae45572daa6748c6234bd212f3e68110f778238
This commit is contained in:
David Novotny
Facebook GitHub Bot
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359
projects/nerf/nerf/nerf_renderer.py
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359
projects/nerf/nerf/nerf_renderer.py
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# Copyright (c) Facebook, Inc. and its affiliates. All rights reserved.
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from typing import Tuple, List, Optional
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import torch
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from pytorch3d.renderer import ImplicitRenderer
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from pytorch3d.renderer.cameras import CamerasBase
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from .implicit_function import NeuralRadianceField
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from .raymarcher import EmissionAbsorptionNeRFRaymarcher
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from .raysampler import NeRFRaysampler, ProbabilisticRaysampler
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from .utils import sample_images_at_mc_locs, calc_psnr, calc_mse
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class RadianceFieldRenderer(torch.nn.Module):
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"""
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Implements a renderer of a Neural Radiance Field.
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This class holds pointers to the fine and coarse renderer objects, which are
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instances of `pytorch3d.renderer.ImplicitRenderer`, and pointers to the
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neural networks representing the fine and coarse Neural Radiance Fields,
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which are instances of `NeuralRadianceField`.
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The rendering forward pass proceeds as follows:
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1) For a given input camera, rendering rays are generated with the
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`NeRFRaysampler` object of `self._renderer['coarse']`.
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In the training mode (`self.training==True`), the rays are a set
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of `n_rays_per_image` random 2D locations of the image grid.
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In the evaluation mode (`self.training==False`), the rays correspond
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to the full image grid. The rays are further split to
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`chunk_size_test`-sized chunks to prevent out-of-memory errors.
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2) For each ray point, the coarse `NeuralRadianceField` MLP is evaluated.
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The pointer to this MLP is stored in `self._implicit_function['coarse']`
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3) The coarse radiance field is rendered with the
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`EmissionAbsorptionNeRFRaymarcher` object of `self._renderer['coarse']`.
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4) The coarse raymarcher outputs a probability distribution that guides
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the importance raysampling of the fine rendering pass. The
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`ProbabilisticRaysampler` stored in `self._renderer['fine'].raysampler`
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implements the importance ray-sampling.
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5) Similar to 2) the fine MLP in `self._implicit_function['fine']`
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labels the ray points with occupancies and colors.
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6) self._renderer['fine'].raymarcher` generates the final fine render.
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7) The fine and coarse renders are compared to the ground truth input image
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with PSNR and MSE metrics.
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"""
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def __init__(
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self,
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image_size: Tuple[int, int],
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n_pts_per_ray: int,
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n_pts_per_ray_fine: int,
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n_rays_per_image: int,
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min_depth: float,
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max_depth: float,
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stratified: bool,
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stratified_test: bool,
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chunk_size_test: int,
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n_harmonic_functions_xyz: int = 6,
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n_harmonic_functions_dir: int = 4,
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n_hidden_neurons_xyz: int = 256,
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n_hidden_neurons_dir: int = 128,
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n_layers_xyz: int = 8,
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append_xyz: List[int] = (5,),
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density_noise_std: float = 0.0,
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):
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"""
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Args:
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image_size: The size of the rendered image (`[height, width]`).
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n_pts_per_ray: The number of points sampled along each ray for the
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coarse rendering pass.
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n_pts_per_ray_fine: The number of points sampled along each ray for the
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fine rendering pass.
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n_rays_per_image: Number of Monte Carlo ray samples when training
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(`self.training==True`).
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min_depth: The minimum depth of a sampled ray-point for the coarse rendering.
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max_depth: The maximum depth of a sampled ray-point for the coarse rendering.
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stratified: If `True`, stratifies (=randomly offsets) the depths
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of each ray point during training (`self.training==True`).
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stratified_test: If `True`, stratifies (=randomly offsets) the depths
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of each ray point during evaluation (`self.training==False`).
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chunk_size_test: The number of rays in each chunk of image rays.
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Active only when `self.training==True`.
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n_harmonic_functions_xyz: The number of harmonic functions
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used to form the harmonic embedding of 3D point locations.
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n_harmonic_functions_dir: The number of harmonic functions
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used to form the harmonic embedding of the ray directions.
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n_hidden_neurons_xyz: The number of hidden units in the
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fully connected layers of the MLP that accepts the 3D point
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locations and outputs the occupancy field with the intermediate
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features.
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n_hidden_neurons_dir: The number of hidden units in the
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fully connected layers of the MLP that accepts the intermediate
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features and ray directions and outputs the radiance field
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(per-point colors).
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n_layers_xyz: The number of layers of the MLP that outputs the
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occupancy field.
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append_xyz: The list of indices of the skip layers of the occupancy MLP.
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Prior to evaluating the skip layers, the tensor which was input to MLP
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is appended to the skip layer input.
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density_noise_std: The standard deviation of the random normal noise
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added to the output of the occupancy MLP.
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Active only when `self.training==True`.
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"""
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super().__init__()
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# The renderers and implicit functions are stored under the fine/coarse
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# keys in ModuleDict PyTorch modules.
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self._renderer = torch.nn.ModuleDict()
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self._implicit_function = torch.nn.ModuleDict()
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# Init the EA raymarcher used by both passes.
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raymarcher = EmissionAbsorptionNeRFRaymarcher()
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# Parse out image dimensions.
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image_height, image_width = image_size
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for render_pass in ("coarse", "fine"):
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if render_pass == "coarse":
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# Initialize the coarse raysampler.
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raysampler = NeRFRaysampler(
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n_pts_per_ray=n_pts_per_ray,
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min_depth=min_depth,
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max_depth=max_depth,
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stratified=stratified,
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stratified_test=stratified_test,
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n_rays_per_image=n_rays_per_image,
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image_height=image_height,
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image_width=image_width,
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)
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elif render_pass == "fine":
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# Initialize the fine raysampler.
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raysampler = ProbabilisticRaysampler(
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n_pts_per_ray=n_pts_per_ray_fine,
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stratified=stratified,
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stratified_test=stratified_test,
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)
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else:
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raise ValueError(f"No such rendering pass {render_pass}")
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# Initialize the fine/coarse renderer.
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self._renderer[render_pass] = ImplicitRenderer(
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raysampler=raysampler,
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raymarcher=raymarcher,
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)
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# Instantiate the fine/coarse NeuralRadianceField module.
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self._implicit_function[render_pass] = NeuralRadianceField(
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n_harmonic_functions_xyz=n_harmonic_functions_xyz,
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n_harmonic_functions_dir=n_harmonic_functions_dir,
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n_hidden_neurons_xyz=n_hidden_neurons_xyz,
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n_hidden_neurons_dir=n_hidden_neurons_dir,
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n_layers_xyz=n_layers_xyz,
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append_xyz=append_xyz,
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)
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self._density_noise_std = density_noise_std
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self._chunk_size_test = chunk_size_test
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self._image_size = image_size
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def precache_rays(
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self,
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cache_cameras: List[CamerasBase],
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cache_camera_hashes: List[str],
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):
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"""
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Precaches the rays emitted from the list of cameras `cache_cameras`,
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where each camera is uniquely identified with the corresponding hash
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from `cache_camera_hashes`.
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The cached rays are moved to cpu and stored in
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`self._renderer['coarse']._ray_cache`.
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Raises `ValueError` when caching two cameras with the same hash.
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Args:
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cache_cameras: A list of `N` cameras for which the rays are pre-cached.
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cache_camera_hashes: A list of `N` unique identifiers for each
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camera from `cameras`.
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"""
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self._renderer["coarse"].raysampler.precache_rays(
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cache_cameras,
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cache_camera_hashes,
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)
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def _process_ray_chunk(
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self,
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camera_hash: Optional[str],
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camera: CamerasBase,
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image: torch.Tensor,
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chunk_idx: int,
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) -> dict:
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"""
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Samples and renders a chunk of rays.
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Args:
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camera_hash: A unique identifier of a pre-cached camera.
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If `None`, the cache is not searched and the sampled rays are
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calculated from scratch.
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camera: A batch of cameras from which the scene is rendered.
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image: A batch of corresponding ground truth images of shape
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('batch_size', ·, ·, 3).
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chunk_idx: The index of the currently rendered ray chunk.
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Returns:
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out: `dict` containing the outputs of the rendering:
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`rgb_coarse`: The result of the coarse rendering pass.
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`rgb_fine`: The result of the fine rendering pass.
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`rgb_gt`: The corresponding ground-truth RGB values.
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"""
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# Initialize the outputs of the coarse rendering to None.
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coarse_ray_bundle = None
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coarse_weights = None
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# First evaluate the coarse rendering pass, then the fine one.
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for renderer_pass in ("coarse", "fine"):
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(rgb, weights), ray_bundle_out = self._renderer[renderer_pass](
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cameras=camera,
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volumetric_function=self._implicit_function[renderer_pass],
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chunksize=self._chunk_size_test,
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chunk_idx=chunk_idx,
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density_noise_std=(self._density_noise_std if self.training else 0.0),
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input_ray_bundle=coarse_ray_bundle,
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ray_weights=coarse_weights,
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camera_hash=camera_hash,
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)
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if renderer_pass == "coarse":
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rgb_coarse = rgb
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# Store the weights and the rays of the first rendering pass
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# for the ensuing importance ray-sampling of the fine render.
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coarse_ray_bundle = ray_bundle_out
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coarse_weights = weights
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if image is not None:
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# Sample the ground truth images at the xy locations of the
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# rendering ray pixels.
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rgb_gt = sample_images_at_mc_locs(
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image[..., :3][None],
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ray_bundle_out.xys,
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)
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else:
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rgb_gt = None
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elif renderer_pass == "fine":
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rgb_fine = rgb
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else:
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raise ValueError(f"No such rendering pass {renderer_pass}")
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return {
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"rgb_fine": rgb_fine,
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"rgb_coarse": rgb_coarse,
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"rgb_gt": rgb_gt,
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# Store the coarse rays/weights only for visualization purposes.
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"coarse_ray_bundle": type(coarse_ray_bundle)(
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*[v.detach().cpu() for k, v in coarse_ray_bundle._asdict().items()]
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),
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"coarse_weights": coarse_weights.detach().cpu(),
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}
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def forward(
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self,
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camera_hash: Optional[str],
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camera: CamerasBase,
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image: torch.Tensor,
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) -> Tuple[dict, dict]:
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"""
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Performs the coarse and fine rendering passees of the radiance field
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from the viewpoint of the input `camera`.
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Afterwards, both renders are compared to the input ground truth `image`
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by evaluating the peak signal-to-noise ratio and the mean-squared error.
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The rendering result depends on the `self.training` flag:
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- In the training mode (`self.training==True`), the function renders
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a random subset of image rays (Monte Carlo rendering).
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- In evaluation mode (`self.training==False`), the function renders
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the full image. In order to prevent out-of-memory errors,
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when `self.training==False`, the rays are sampled and rendered
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in batches of size `chunksize`.
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Args:
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camera_hash: A unique identifier of a pre-cached camera.
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If `None`, the cache is not searched and the sampled rays are
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calculated from scratch.
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camera: A batch of cameras from which the scene is rendered.
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image: A batch of corresponding ground truth images of shape
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('batch_size', ·, ·, 3).
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Returns:
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out: `dict` containing the outputs of the rendering:
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`rgb_coarse`: The result of the coarse rendering pass.
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`rgb_fine`: The result of the fine rendering pass.
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`rgb_gt`: The corresponding ground-truth RGB values.
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The shape of `rgb_coarse`, `rgb_fine`, `rgb_gt` depends on the
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`self.training` flag:
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If `==True`, all 3 tensors are of shape
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`(batch_size, n_rays_per_image, 3)` and contain the result
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of the Monte Carlo training rendering pass.
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If `==False`, all 3 tensors are of shape
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`(batch_size, image_size[0], image_size[1], 3)` and contain
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the result of the full image rendering pass.
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metrics: `dict` containing the error metrics comparing the fine and
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coarse renders to the ground truth:
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`mse_coarse`: Mean-squared error between the coarse render and
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the input `image`
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`mse_fine`: Mean-squared error between the fine render and
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the input `image`
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`psnr_coarse`: Peak signal-to-noise ratio between the coarse render and
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the input `image`
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`psnr_fine`: Peak signal-to-noise ratio between the fine render and
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the input `image`
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"""
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if not self.training:
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# Full evaluation pass.
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n_chunks = self._renderer["coarse"].raysampler.get_n_chunks(
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self._chunk_size_test,
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camera.R.shape[0],
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)
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else:
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# MonteCarlo ray sampling.
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n_chunks = 1
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# Process the chunks of rays.
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chunk_outputs = [
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self._process_ray_chunk(
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camera_hash,
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camera,
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image,
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chunk_idx,
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)
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for chunk_idx in range(n_chunks)
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]
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if not self.training:
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# For a full render pass concatenate the output chunks,
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# and reshape to image size.
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out = {
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k: torch.cat(
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[ch_o[k] for ch_o in chunk_outputs],
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dim=1,
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).view(-1, *self._image_size, 3)
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if chunk_outputs[0][k] is not None
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else None
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for k in ("rgb_fine", "rgb_coarse", "rgb_gt")
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}
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else:
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out = chunk_outputs[0]
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# Calc the error metrics.
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metrics = {}
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if image is not None:
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for render_pass in ("coarse", "fine"):
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for metric_name, metric_fun in zip(
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("mse", "psnr"), (calc_mse, calc_psnr)
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):
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metrics[f"{metric_name}_{render_pass}"] = metric_fun(
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out["rgb_" + render_pass][..., :3],
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out["rgb_gt"][..., :3],
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)
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return out, metrics
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@ -65,3 +65,55 @@ def sample_pdf(
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samples = bins_g[..., 0] + t * (bins_g[..., 1] - bins_g[..., 0])
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return samples
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def calc_mse(x: torch.Tensor, y: torch.Tensor):
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"""
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Calculates the mean square error between tensors `x` and `y`.
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"""
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return torch.mean((x - y) ** 2)
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def calc_psnr(x: torch.Tensor, y: torch.Tensor):
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"""
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Calculates the Peak-signal-to-noise ratio between tensors `x` and `y`.
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"""
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mse = calc_mse(x, y)
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psnr = -10.0 * torch.log10(mse)
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return psnr
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def sample_images_at_mc_locs(
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target_images: torch.Tensor,
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sampled_rays_xy: torch.Tensor,
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):
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"""
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Given a set of pixel locations `sampled_rays_xy` this method samples the tensor
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`target_images` at the respective 2D locations.
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This function is used in order to extract the colors from ground truth images
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that correspond to the colors rendered using a Monte Carlo rendering.
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Args:
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target_images: A tensor of shape `(batch_size, ..., 3)`.
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sampled_rays_xy: A tensor of shape `(batch_size, S_1, ..., S_N, 2)`.
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Returns:
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images_sampled: A tensor of shape `(batch_size, S_1, ..., S_N, 3)`
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containing `target_images` sampled at `sampled_rays_xy`.
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"""
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ba = target_images.shape[0]
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dim = target_images.shape[-1]
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spatial_size = sampled_rays_xy.shape[1:-1]
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# The coordinate grid convention for grid_sample has both x and y
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# directions inverted.
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xy_sample = -sampled_rays_xy.view(ba, -1, 1, 2).clone()
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images_sampled = torch.nn.functional.grid_sample(
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target_images.permute(0, 3, 1, 2),
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xy_sample,
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align_corners=True,
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mode="bilinear",
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)
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return images_sampled.permute(0, 2, 3, 1).view(ba, *spatial_size, dim)
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