lighting

pytorch3d.renderer.lighting.diffuse(normals, color, direction) → torch.Tensor

Calculate the diffuse component of light reflection using Lambert’s cosine law.

Parameters:

• normals – (N, …, 3) xyz normal vectors. Normals and points are expected to have the same shape.
• color – (1, 3) or (N, 3) RGB color of the diffuse component of the light.
• direction – (x,y,z) direction of the light

Returns:

colors – (N, …, 3), same shape as the input points.

The normals and light direction should be in the same coordinate frame i.e. if the points have been transformed from world -> view space then the normals and direction should also be in view space.

NOTE: to use with the packed vertices (i.e. no batch dimension) reformat the inputs in the following way.

Args:
    normals: (P, 3)
    color: (N, 3)[batch_idx, :] -> (P, 3)
    direction: (N, 3)[batch_idx, :] -> (P, 3)

Returns:
    colors: (P, 3)

where batch_idx is of shape (P). For meshes, batch_idx can be:
meshes.verts_packed_to_mesh_idx() or meshes.faces_packed_to_mesh_idx()
depending on whether points refers to the vertex coordinates or
average/interpolated face coordinates.

pytorch3d.renderer.lighting.specular(points, normals, direction, color, camera_position, shininess) → torch.Tensor

Calculate the specular component of light reflection.

Parameters:

• points – (N, …, 3) xyz coordinates of the points.
• normals – (N, …, 3) xyz normal vectors for each point.
• color – (N, 3) RGB color of the specular component of the light.
• direction – (N, 3) vector direction of the light.
• camera_position – (N, 3) The xyz position of the camera.
• shininess –
  14. The specular exponent of the material.

Returns:

colors – (N, …, 3), same shape as the input points.

The points, normals, camera_position, and direction should be in the same coordinate frame i.e. if the points have been transformed from world -> view space then the normals, camera_position, and light direction should also be in view space.

To use with a batch of packed points reindex in the following way. .. code-block:: python:

Args:
    points: (P, 3)
    normals: (P, 3)
    color: (N, 3)[batch_idx] -> (P, 3)
    direction: (N, 3)[batch_idx] -> (P, 3)
    camera_position: (N, 3)[batch_idx] -> (P, 3)
    shininess: (N)[batch_idx] -> (P)
Returns:
    colors: (P, 3)

where batch_idx is of shape (P). For meshes batch_idx can be:
meshes.verts_packed_to_mesh_idx() or meshes.faces_packed_to_mesh_idx().

class pytorch3d.renderer.lighting.DirectionalLights(ambient_color=((0.5, 0.5, 0.5), ), diffuse_color=((0.3, 0.3, 0.3), ), specular_color=((0.2, 0.2, 0.2), ), direction=((0, 1, 0), ), device: Union[str, torch.device] = 'cpu')

__init__(ambient_color=((0.5, 0.5, 0.5), ), diffuse_color=((0.3, 0.3, 0.3), ), specular_color=((0.2, 0.2, 0.2), ), direction=((0, 1, 0), ), device: Union[str, torch.device] = 'cpu') → None

Parameters:

• ambient_color – RGB color of the ambient component.
• diffuse_color – RGB color of the diffuse component.
• specular_color – RGB color of the specular component.
• direction – (x, y, z) direction vector of the light.
• device – Device (as str or torch.device) on which the tensors should be located

The inputs can each be

• 3 element tuple/list or list of lists
• torch tensor of shape (1, 3)
• torch tensor of shape (N, 3)

The inputs are broadcast against each other so they all have batch dimension N.

clone()

diffuse(normals, points=None) → torch.Tensor

specular(normals, points, camera_position, shininess) → torch.Tensor

class pytorch3d.renderer.lighting.PointLights(ambient_color=((0.5, 0.5, 0.5), ), diffuse_color=((0.3, 0.3, 0.3), ), specular_color=((0.2, 0.2, 0.2), ), location=((0, 1, 0), ), device: Union[str, torch.device] = 'cpu')

__init__(ambient_color=((0.5, 0.5, 0.5), ), diffuse_color=((0.3, 0.3, 0.3), ), specular_color=((0.2, 0.2, 0.2), ), location=((0, 1, 0), ), device: Union[str, torch.device] = 'cpu') → None

Parameters:

• ambient_color – RGB color of the ambient component
• diffuse_color – RGB color of the diffuse component
• specular_color – RGB color of the specular component
• location – xyz position of the light.
• device – Device (as str or torch.device) on which the tensors should be located

The inputs can each be

• 3 element tuple/list or list of lists
• torch tensor of shape (1, 3)
• torch tensor of shape (N, 3)

The inputs are broadcast against each other so they all have batch dimension N.

clone()

reshape_location(points) → torch.Tensor

Reshape the location tensor to have dimensions compatible with the points which can either be of shape (P, 3) or (N, H, W, K, 3).

diffuse(normals, points) → torch.Tensor

specular(normals, points, camera_position, shininess) → torch.Tensor

class pytorch3d.renderer.lighting.AmbientLights(*, ambient_color=None, device: Union[str, torch.device] = 'cpu')

A light object representing the same color of light everywhere. By default, this is white, which effectively means lighting is not used in rendering.

__init__(*, ambient_color=None, device: Union[str, torch.device] = 'cpu') → None

If ambient_color is provided, it should be a sequence of triples of floats.

Parameters:

• ambient_color – RGB color
• device – Device (as str or torch.device) on which the tensors should be located

The ambient_color if provided, should be

• 3 element tuple/list or list of lists
• torch tensor of shape (1, 3)
• torch tensor of shape (N, 3)

clone()

diffuse(normals, points) → torch.Tensor

specular(normals, points, camera_position, shininess) → torch.Tensor