Cameras#

Cameras#

Camera Models

class nerfstudio.cameras.cameras.CameraType(value)[source]#

Bases: Enum

Supported camera types.

class nerfstudio.cameras.cameras.Cameras(camera_to_worlds: Float[Tensor, '*batch_c2ws 3 4'], fx: Union[Float[Tensor, '*batch_fxs 1'], float], fy: Union[Float[Tensor, '*batch_fys 1'], float], cx: Union[Float[Tensor, '*batch_cxs 1'], float], cy: Union[Float[Tensor, '*batch_cys 1'], float], width: Optional[Union[Shaped[Tensor, '*batch_ws 1'], int]] = None, height: Optional[Union[Shaped[Tensor, '*batch_hs 1'], int]] = None, distortion_params: Optional[Float[Tensor, '*batch_dist_params 6']] = None, camera_type: Union[Int[Tensor, '*batch_cam_types 1'], int, List[CameraType], CameraType] = CameraType.PERSPECTIVE, times: Optional[Float[Tensor, 'num_cameras']] = None, metadata: Optional[Dict] = None)[source]#

Bases: TensorDataclass

Dataparser outputs for the image dataset and the ray generator.

If a single value is provided, it is broadcasted to all cameras.

Parameters:
  • camera_to_worlds – Camera to world matrices. Tensor of per-image c2w matrices, in [R | t] format

  • fx – Focal length x

  • fy – Focal length y

  • cx – Principal point x

  • cy – Principal point y

  • width – Image width

  • height – Image height

  • distortion_params – OpenCV 6 radial distortion coefficients

  • camera_type – Type of camera model. This will be an int corresponding to the CameraType enum.

  • times – Timestamps for each camera

  • metadata – Additional metadata or data needed for interpolation, will mimic shape of the cameras and will be broadcasted to the rays generated from any derivative RaySamples we create with this

property device: Union[device, str]#

Returns the device that the camera is on.

generate_rays(camera_indices: Union[Int[Tensor, '*num_rays num_cameras_batch_dims'], int], coords: Optional[Float[Tensor, '*num_rays 2']] = None, camera_opt_to_camera: Optional[Float[Tensor, '*num_rays 3 4']] = None, distortion_params_delta: Optional[Float[Tensor, '*num_rays 6']] = None, keep_shape: Optional[bool] = None, disable_distortion: bool = False, aabb_box: Optional[SceneBox] = None, obb_box: Optional[OrientedBox] = None) RayBundle[source]#

Generates rays for the given camera indices.

This function will standardize the input arguments and then call the _generate_rays_from_coords function to generate the rays. Our goal is to parse the arguments and then get them into the right shape:

  • camera_indices: (num_rays:…, num_cameras_batch_dims)

  • coords: (num_rays:…, 2)

  • camera_opt_to_camera: (num_rays:…, 3, 4) or None

  • distortion_params_delta: (num_rays:…, 6) or None

Read the docstring for _generate_rays_from_coords for more information on how we generate the rays after we have standardized the arguments.

We are only concerned about different combinations of camera_indices and coords matrices, and the following are the 4 cases we have to deal with:

  1. isinstance(camera_indices, int) and coords == None
    • In this case we broadcast our camera_indices / coords shape (h, w, 1 / 2 respectively)

  2. isinstance(camera_indices, int) and coords != None
    • In this case, we broadcast camera_indices to the same batch dim as coords

  3. not isinstance(camera_indices, int) and coords == None
    • In this case, we will need to set coords so that it is of shape (h, w, num_rays, 2), and broadcast

      all our other args to match the new definition of num_rays := (h, w) + num_rays

  4. not isinstance(camera_indices, int) and coords != None
    • In this case, we have nothing to do, only check that the arguments are of the correct shape

There is one more edge case we need to be careful with: when we have “jagged cameras” (ie: different heights and widths for each camera). This isn’t problematic when we specify coords, since coords is already a tensor. When coords == None (ie: when we render out the whole image associated with this camera), we run into problems since there’s no way to stack each coordinate map as all coordinate maps are all different shapes. In this case, we will need to flatten each individual coordinate map and concatenate them, giving us only one batch dimension, regardless of the number of prepended extra batch dimensions in the camera_indices tensor.

Parameters:
  • camera_indices – Camera indices of the flattened cameras object to generate rays for.

  • coords – Coordinates of the pixels to generate rays for. If None, the full image will be rendered.

  • camera_opt_to_camera – Optional transform for the camera to world matrices.

  • distortion_params_delta – Optional delta for the distortion parameters.

  • keep_shape – If None, then we default to the regular behavior of flattening if cameras is jagged, otherwise keeping dimensions. If False, we flatten at the end. If True, then we keep the shape of the camera_indices and coords tensors (if we can).

  • disable_distortion – If True, disables distortion.

  • aabb_box – if not None will calculate nears and fars of the ray according to aabb box intersection

Returns:

Rays for the given camera indices and coords.

get_image_coords(pixel_offset: float = 0.5, index: Optional[Tuple] = None) Float[Tensor, 'height width 2'][source]#

This gets the image coordinates of one of the cameras in this object.

If no index is specified, it will return the maximum possible sized height / width image coordinate map, by looking at the maximum height and width of all the cameras in this object.

Parameters:
  • pixel_offset – Offset for each pixel. Defaults to center of pixel (0.5)

  • index – Tuple of indices into the batch dimensions of the camera. Defaults to None, which returns the 0th flattened camera

Returns:

Grid of image coordinates.

get_intrinsics_matrices() Float[Tensor, '*num_cameras 3 3'][source]#

Returns the intrinsic matrices for each camera.

Returns:

Pinhole camera intrinsics matrices

property image_height: Shaped[Tensor, '*num_cameras 1']#

Returns the height of the images.

property image_width: Shaped[Tensor, '*num_cameras 1']#

Returns the height of the images.

property is_jagged: bool#

Returns whether or not the cameras are “jagged” (i.e. the height and widths are different, meaning that you cannot concatenate the image coordinate maps together)

rescale_output_resolution(scaling_factor: Union[Shaped[Tensor, '*num_cameras'], Shaped[Tensor, '*num_cameras 1'], float, int]) None[source]#

Rescale the output resolution of the cameras.

Parameters:

scaling_factor – Scaling factor to apply to the output resolution.

to_json(camera_idx: int, image: Optional[Float[Tensor, 'height width 2']] = None, max_size: Optional[int] = None) Dict[source]#

Convert a camera to a json dictionary.

Parameters:
  • camera_idx – Index of the camera to convert.

  • image – An image in range [0, 1] that is encoded to a base64 string.

  • max_size – Max size to resize the image to if present.

Returns:

A JSON representation of the camera

Camera Optimizers#

Pose and Intrinsics Optimizers

class nerfstudio.cameras.camera_optimizers.CameraOptimizer(config: CameraOptimizerConfig, num_cameras: int, device: Union[device, str], non_trainable_camera_indices: Optional[Int[Tensor, 'num_non_trainable_cameras']] = None, **kwargs)[source]#

Bases: Module

Layer that modifies camera poses to be optimized as well as the field during training.

apply_to_raybundle(raybundle: RayBundle) None[source]#

Apply the pose correction to the raybundle

forward(indices: Int[Tensor, 'camera_indices']) Float[Tensor, 'camera_indices 3 4'][source]#

Indexing into camera adjustments. :param indices: indices of Cameras to optimize.

Returns:

Transformation matrices from optimized camera coordinates to given camera coordinates.

get_correction_matrices()[source]#

Get optimized pose correction matrices

get_loss_dict(loss_dict: dict) None[source]#

Add regularization

get_metrics_dict(metrics_dict: dict) None[source]#

Get camera optimizer metrics

get_param_groups(param_groups: dict) None[source]#

Get camera optimizer parameters

class nerfstudio.cameras.camera_optimizers.CameraOptimizerConfig(_target: ~typing.Type = <factory>, mode: ~typing.Literal['off', 'SO3xR3', 'SE3'] = 'off', trans_l2_penalty: float = 0.01, rot_l2_penalty: float = 0.001, optimizer: ~typing.Optional[~nerfstudio.engine.optimizers.OptimizerConfig] = None, scheduler: ~typing.Optional[~nerfstudio.engine.schedulers.SchedulerConfig] = None)[source]#

Bases: InstantiateConfig

Configuration of optimization for camera poses.

mode: Literal['off', 'SO3xR3', 'SE3'] = 'off'#

Pose optimization strategy to use. If enabled, we recommend SO3xR3.

optimizer: Optional[OptimizerConfig] = None#

Deprecated, now specified inside the optimizers dict

rot_l2_penalty: float = 0.001#

L2 penalty on rotation parameters.

scheduler: Optional[SchedulerConfig] = None#

Deprecated, now specified inside the optimizers dict

trans_l2_penalty: float = 0.01#

L2 penalty on translation parameters.

Camera Paths#

Code for camera paths.

nerfstudio.cameras.camera_paths.get_interpolated_camera_path(cameras: Cameras, steps: int, order_poses: bool) Cameras[source]#

Generate a camera path between two cameras. Uses the camera type of the first camera

Parameters:
  • cameras – Cameras object containing intrinsics of all cameras.

  • steps – The number of steps to interpolate between the two cameras.

Returns:

A new set of cameras along a path.

nerfstudio.cameras.camera_paths.get_path_from_json(camera_path: Dict[str, Any]) Cameras[source]#

Takes a camera path dictionary and returns a trajectory as a Camera instance.

Parameters:

camera_path – A dictionary of the camera path information coming from the viewer.

Returns:

A Cameras instance with the camera path.

nerfstudio.cameras.camera_paths.get_spiral_path(camera: Cameras, steps: int = 30, radius: Optional[float] = None, radiuses: Optional[Tuple[float]] = None, rots: int = 2, zrate: float = 0.5) Cameras[source]#

Returns a list of camera in a spiral trajectory.

Parameters:
  • camera – The camera to start the spiral from.

  • steps – The number of cameras in the generated path.

  • radius – The radius of the spiral for all xyz directions.

  • radiuses – The list of radii for the spiral in xyz directions.

  • rots – The number of rotations to apply to the camera.

  • zrate – How much to change the z position of the camera.

Returns:

A spiral camera path.

Camera Utils#

Camera transformation helper code.

nerfstudio.cameras.camera_utils.auto_orient_and_center_poses(poses: Float[Tensor, '*num_poses 4 4'], method: Literal['pca', 'up', 'vertical', 'none'] = 'up', center_method: Literal['poses', 'focus', 'none'] = 'poses') Tuple[Float[Tensor, '*num_poses 3 4'], Float[Tensor, '3 4']][source]#

Orients and centers the poses.

We provide three methods for orientation:

  • pca: Orient the poses so that the principal directions of the camera centers are aligned

    with the axes, Z corresponding to the smallest principal component. This method works well when all of the cameras are in the same plane, for example when images are taken using a mobile robot.

  • up: Orient the poses so that the average up vector is aligned with the z axis.

    This method works well when images are not at arbitrary angles.

  • vertical: Orient the poses so that the Z 3D direction projects close to the

    y axis in images. This method works better if cameras are not all looking in the same 3D direction, which may happen in camera arrays or in LLFF.

There are two centering methods:

  • poses: The poses are centered around the origin.

  • focus: The origin is set to the focus of attention of all cameras (the

    closest point to cameras optical axes). Recommended for inward-looking camera configurations.

Parameters:
  • poses – The poses to orient.

  • method – The method to use for orientation.

  • center_method – The method to use to center the poses.

Returns:

Tuple of the oriented poses and the transform matrix.

nerfstudio.cameras.camera_utils.focus_of_attention(poses: Float[Tensor, '*num_poses 4 4'], initial_focus: Float[Tensor, '3']) Float[Tensor, '3'][source]#

Compute the focus of attention of a set of cameras. Only cameras that have the focus of attention in front of them are considered.

Args:

poses: The poses to orient. initial_focus: The 3D point views to decide which cameras are initially activated.

Returns:

The 3D position of the focus of attention.

nerfstudio.cameras.camera_utils.get_distortion_params(k1: float = 0.0, k2: float = 0.0, k3: float = 0.0, k4: float = 0.0, p1: float = 0.0, p2: float = 0.0) Float[Tensor, '*batch'][source]#

Returns a distortion parameters matrix.

Parameters:
  • k1 – The first radial distortion parameter.

  • k2 – The second radial distortion parameter.

  • k3 – The third radial distortion parameter.

  • k4 – The fourth radial distortion parameter.

  • p1 – The first tangential distortion parameter.

  • p2 – The second tangential distortion parameter.

Returns:

A distortion parameters matrix.

Return type:

torch.Tensor

nerfstudio.cameras.camera_utils.get_interpolated_k(k_a: Float[Tensor, '3 3'], k_b: Float[Tensor, '3 3'], steps: int = 10) List[Float[Tensor, '3 4']][source]#

Returns interpolated path between two camera poses with specified number of steps.

Parameters:
  • k_a – camera matrix 1

  • k_b – camera matrix 2

  • steps – number of steps the interpolated pose path should contain

Returns:

List of interpolated camera poses

nerfstudio.cameras.camera_utils.get_interpolated_poses(pose_a: ndarray[Any, dtype[_ScalarType_co]], pose_b: ndarray[Any, dtype[_ScalarType_co]], steps: int = 10) List[float][source]#

Return interpolation of poses with specified number of steps. :param pose_a: first pose :param pose_b: second pose :param steps: number of steps the interpolated pose path should contain

nerfstudio.cameras.camera_utils.get_interpolated_poses_many(poses: Float[Tensor, 'num_poses 3 4'], Ks: Float[Tensor, 'num_poses 3 3'], steps_per_transition: int = 10, order_poses: bool = False) Tuple[Float[Tensor, 'num_poses 3 4'], Float[Tensor, 'num_poses 3 3']][source]#

Return interpolated poses for many camera poses.

Parameters:
  • poses – list of camera poses

  • Ks – list of camera intrinsics

  • steps_per_transition – number of steps per transition

  • order_poses – whether to order poses by euclidian distance

Returns:

tuple of new poses and intrinsics

nerfstudio.cameras.camera_utils.get_ordered_poses_and_k(poses: Float[Tensor, 'num_poses 3 4'], Ks: Float[Tensor, 'num_poses 3 3']) Tuple[Float[Tensor, 'num_poses 3 4'], Float[Tensor, 'num_poses 3 3']][source]#

Returns ordered poses and intrinsics by euclidian distance between poses.

Parameters:
  • poses – list of camera poses

  • Ks – list of camera intrinsics

Returns:

tuple of ordered poses and intrinsics

nerfstudio.cameras.camera_utils.normalize(x: Tensor) Float[Tensor, '*batch'][source]#

Returns a normalized vector.

nerfstudio.cameras.camera_utils.normalize_with_norm(x: Tensor, dim: int) Tuple[Tensor, Tensor][source]#

Normalize tensor along axis and return normalized value with norms.

Parameters:
  • x – tensor to normalize.

  • dim – axis along which to normalize.

Returns:

Tuple of normalized tensor and corresponding norm.

nerfstudio.cameras.camera_utils.quaternion_from_matrix(matrix: ndarray[Any, dtype[_ScalarType_co]], isprecise: bool = False) ndarray[source]#

Return quaternion from rotation matrix.

Parameters:
  • matrix – rotation matrix to obtain quaternion

  • isprecise – if True, input matrix is assumed to be precise rotation matrix and a faster algorithm is used.

nerfstudio.cameras.camera_utils.quaternion_matrix(quaternion: ndarray[Any, dtype[_ScalarType_co]]) ndarray[source]#

Return homogeneous rotation matrix from quaternion.

Parameters:

quaternion – value to convert to matrix

nerfstudio.cameras.camera_utils.quaternion_slerp(quat0: ndarray[Any, dtype[_ScalarType_co]], quat1: ndarray[Any, dtype[_ScalarType_co]], fraction: float, spin: int = 0, shortestpath: bool = True) ndarray[source]#

Return spherical linear interpolation between two quaternions. :param quat0: first quaternion :param quat1: second quaternion :param fraction: how much to interpolate between quat0 vs quat1 (if 0, closer to quat0; if 1, closer to quat1) :param spin: how much of an additional spin to place on the interpolation :param shortestpath: whether to return the short or long path to rotation

nerfstudio.cameras.camera_utils.radial_and_tangential_undistort(coords: Tensor, distortion_params: Tensor, eps: float = 0.001, max_iterations: int = 10) Tensor[source]#

Computes undistorted coords given opencv distortion parameters. Adapted from MultiNeRF https://github.com/google-research/multinerf/blob/b02228160d3179300c7d499dca28cb9ca3677f32/internal/camera_utils.py#L477-L509

Parameters:
  • coords – The distorted coordinates.

  • distortion_params – The distortion parameters [k1, k2, k3, k4, p1, p2].

  • eps – The epsilon for the convergence.

  • max_iterations – The maximum number of iterations to perform.

Returns:

The undistorted coordinates.

nerfstudio.cameras.camera_utils.rotation_matrix(a: Float[Tensor, '3'], b: Float[Tensor, '3']) Float[Tensor, '3 3'][source]#

Compute the rotation matrix that rotates vector a to vector b.

Parameters:
  • a – The vector to rotate.

  • b – The vector to rotate to.

Returns:

The rotation matrix.

nerfstudio.cameras.camera_utils.unit_vector(data: ndarray[Any, dtype[_ScalarType_co]], axis: Optional[int] = None) ndarray[source]#

Return ndarray normalized by length, i.e. Euclidean norm, along axis.

Parameters:
  • axis – the axis along which to normalize into unit vector

  • out – where to write out the data to. If None, returns a new np ndarray

nerfstudio.cameras.camera_utils.viewmatrix(lookat: Tensor, up: Tensor, pos: Tensor) Float[Tensor, '*batch'][source]#

Returns a camera transformation matrix.

Parameters:
  • lookat – The direction the camera is looking.

  • up – The upward direction of the camera.

  • pos – The position of the camera.

Returns:

A camera transformation matrix.

Lie Groups#

Helper for Lie group operations. Currently only used for pose optimization.

nerfstudio.cameras.lie_groups.exp_map_SE3(tangent_vector: Float[Tensor, 'b 6']) Float[Tensor, 'b 3 4'][source]#

Compute the exponential map se(3) -> SE(3).

This can be used for learning pose deltas on SE(3).

Parameters:

tangent_vector – A tangent vector from se(3).

Returns:

[R|t] transformation matrices.

nerfstudio.cameras.lie_groups.exp_map_SO3xR3(tangent_vector: Float[Tensor, 'b 6']) Float[Tensor, 'b 3 4'][source]#

Compute the exponential map of the direct product group SO(3) x R^3.

This can be used for learning pose deltas on SE(3), and is generally faster than exp_map_SE3.

Parameters:

tangent_vector – Tangent vector; length-3 translations, followed by an so(3) tangent vector.

Returns:

[R|t] transformation matrices.

Rays#

Some ray datastructures.

class nerfstudio.cameras.rays.Frustums(origins: Float[Tensor, '*bs 3'], directions: Float[Tensor, '*bs 3'], starts: Float[Tensor, '*bs 1'], ends: Float[Tensor, '*bs 1'], pixel_area: Float[Tensor, '*bs 1'], offsets: Optional[Float[Tensor, '*bs 3']] = None)[source]#

Bases: TensorDataclass

Describes region of space as a frustum.

directions: Float[Tensor, '*bs 3']#

Direction of ray.

ends: Float[Tensor, '*bs 1']#

Where the frustum ends along a ray.

get_gaussian_blob() Gaussians[source]#

Calculates guassian approximation of conical frustum.

Returns:

Conical frustums approximated by gaussian distribution.

classmethod get_mock_frustum(device: Optional[Union[str, device]] = 'cpu') Frustums[source]#

Helper function to generate a placeholder frustum.

Returns:

A size 1 frustum with meaningless values.

get_positions() Float[Tensor, '*batch 3'][source]#

Calculates “center” position of frustum. Not weighted by mass.

Returns:

xyz positions.

get_start_positions() Float[Tensor, '*batch 3'][source]#

Calculates “start” position of frustum.

Returns:

xyz positions.

offsets: Optional[Float[Tensor, '*bs 3']] = None#

Offsets for each sample position

origins: Float[Tensor, '*bs 3']#

xyz coordinate for ray origin.

pixel_area: Float[Tensor, '*bs 1']#

Projected area of pixel a distance 1 away from origin.

set_offsets(offsets)[source]#

Sets offsets for this frustum for computing positions

starts: Float[Tensor, '*bs 1']#

Where the frustum starts along a ray.

class nerfstudio.cameras.rays.RayBundle(origins: ~jaxtyping.Float[Tensor, '*batch 3'], directions: ~jaxtyping.Float[Tensor, '*batch 3'], pixel_area: ~jaxtyping.Float[Tensor, '*batch 1'], camera_indices: ~typing.Optional[~jaxtyping.Int[Tensor, '*batch 1']] = None, nears: ~typing.Optional[~jaxtyping.Float[Tensor, '*batch 1']] = None, fars: ~typing.Optional[~jaxtyping.Float[Tensor, '*batch 1']] = None, metadata: ~typing.Dict[str, ~jaxtyping.Shaped[Tensor, 'num_rays latent_dims']] = <factory>, times: ~typing.Optional[~jaxtyping.Float[Tensor, '*batch 1']] = None)[source]#

Bases: TensorDataclass

A bundle of ray parameters.

camera_indices: Optional[Int[Tensor, '*batch 1']] = None#

Camera indices

directions: Float[Tensor, '*batch 3']#

Unit ray direction vector

fars: Optional[Float[Tensor, '*batch 1']] = None#

Rays Distance along ray to stop sampling

get_ray_samples(bin_starts: Float[Tensor, '*bs num_samples 1'], bin_ends: Float[Tensor, '*bs num_samples 1'], spacing_starts: Optional[Float[Tensor, '*bs num_samples 1']] = None, spacing_ends: Optional[Float[Tensor, '*bs num_samples 1']] = None, spacing_to_euclidean_fn: Optional[Callable] = None) RaySamples[source]#

Produces samples for each ray by projection points along the ray direction. Currently samples uniformly.

Parameters:
  • bin_starts – Distance from origin to start of bin.

  • bin_ends – Distance from origin to end of bin.

Returns:

Samples projected along ray.

get_row_major_sliced_ray_bundle(start_idx: int, end_idx: int) RayBundle[source]#

Flattens RayBundle and extracts chunk given start and end indices.

Parameters:
  • start_idx – Start index of RayBundle chunk.

  • end_idx – End index of RayBundle chunk.

Returns:

Flattened RayBundle with end_idx-start_idx rays.

metadata: Dict[str, Shaped[Tensor, 'num_rays latent_dims']]#

Additional metadata or data needed for interpolation, will mimic shape of rays

nears: Optional[Float[Tensor, '*batch 1']] = None#

Distance along ray to start sampling

origins: Float[Tensor, '*batch 3']#

Ray origins (XYZ)

pixel_area: Float[Tensor, '*batch 1']#

Projected area of pixel a distance 1 away from origin

sample(num_rays: int) RayBundle[source]#

Returns a RayBundle as a subset of rays.

Parameters:

num_rays – Number of rays in output RayBundle

Returns:

RayBundle with subset of rays.

set_camera_indices(camera_index: int) None[source]#

Sets all the camera indices to a specific camera index.

Parameters:

camera_index – Camera index.

times: Optional[Float[Tensor, '*batch 1']] = None#

Times at which rays are sampled

class nerfstudio.cameras.rays.RaySamples(frustums: Frustums, camera_indices: Optional[Int[Tensor, '*bs 1']] = None, deltas: Optional[Float[Tensor, '*bs 1']] = None, spacing_starts: Optional[Float[Tensor, '*bs num_samples 1']] = None, spacing_ends: Optional[Float[Tensor, '*bs num_samples 1']] = None, spacing_to_euclidean_fn: Optional[Callable] = None, metadata: Optional[Dict[str, Shaped[Tensor, '*bs latent_dims']]] = None, times: Optional[Float[Tensor, '*batch 1']] = None)[source]#

Bases: TensorDataclass

Samples along a ray

camera_indices: Optional[Int[Tensor, '*bs 1']] = None#

Camera index.

deltas: Optional[Float[Tensor, '*bs 1']] = None#

“width” of each sample.

frustums: Frustums#

Frustums along ray.

get_weights(densities: Float[Tensor, '*batch num_samples 1']) Float[Tensor, '*batch num_samples 1'][source]#

Return weights based on predicted densities

Parameters:

densities – Predicted densities for samples along ray

Returns:

Weights for each sample

static get_weights_and_transmittance_from_alphas(alphas: Float[Tensor, '*batch num_samples 1'], weights_only: Literal[True]) Float[Tensor, '*batch num_samples 1'][source]#
static get_weights_and_transmittance_from_alphas(alphas: Float[Tensor, '*batch num_samples 1'], weights_only: Literal[False] = False) Tuple[Float[Tensor, '*batch num_samples 1'], Float[Tensor, '*batch num_samples 1']]

Return weights based on predicted alphas :param alphas: Predicted alphas (maybe from sdf) for samples along ray :param weights_only: If function should return only weights

Returns:

Tuple of weights and transmittance for each sample

metadata: Optional[Dict[str, Shaped[Tensor, '*bs latent_dims']]] = None#

additional information relevant to generating ray samples

spacing_ends: Optional[Float[Tensor, '*bs num_samples 1']] = None#

Start of normalized bin edges along ray [0,1], before warping is applied, ie. linear in disparity sampling.

spacing_starts: Optional[Float[Tensor, '*bs num_samples 1']] = None#

Start of normalized bin edges along ray [0,1], before warping is applied, ie. linear in disparity sampling.

spacing_to_euclidean_fn: Optional[Callable] = None#

Function to convert bins to euclidean distance.

times: Optional[Float[Tensor, '*batch 1']] = None#

Times at which rays are sampled