visu3d - 3D geometry made easy

visu3d is an abstraction layer between Torch/TF/Jax/Numpy and your program. It provides:

Standard primitives for 3d geometry (Ray, Camera, Transform,...). You can combine those standard primitives with your custom ones.
Everything is trivially visualizable with zero boilerplate. Inspect & debug camera poses, trajectories,...
All primitives are dataclass_array, dataclasses which can be reshaped, sliced,... as if they were numpy arrays.
Everything is extensible, you can gradually opt-in only for the features you need, and replace any standard primitive by your custom ones.

Core features

Everything is a v3d.DataclassArray: dataclass behave like np.array (with indexing, slicing, shape manipulation, vectorization,...).

# Single ray
ray = v3d.Ray(pos=[0, 0, 0], dir=[1, 1, 1])
assert rays.shape == ()

# Multiple rays batched together
rays = v3d.Ray(pos=np.zeros((B, H, W, 3)), dir=np.ones((B, H, W, 3)))
assert rays.shape == (B, H, W)

rays = rays.reshape('b h w -> b (h w)')  #  Native `einops` support

top_left_ray = rays[..., 0, 0]  #  (b, h, w) -> (b,)

rays = rays.flatten()
rays = rays[rays.norm() > 0]  # Filter invalid rays

Everything is visualizable interactively

Every object has a .fig property for interactive visualization:

rays.fig  # Plot the rays

Display multiple objects together:

v3d.make_fig([cam, rays, point_cloud])

Auto-plot figures with Colab magic:

v3d.auto_plot_figs()  # Once at the start of the Colab

cam, rays, point_cloud  # Tuple auto-displayed without `v3d.make_fig` call

Same code seamlessly works across Torch, Jax, TensorFlow, Numpy.

rays = rays.as_jax()  # .as_tf(), as_np(), .as_jax()
assert isinstance(rays.pos, jnp.ndarray)
assert rays.xnp is jnp

rays = rays.as_torch()
assert isinstance(rays.pos, torch.Tensor)
assert rays.xnp is torch

With native support for auto-diff, jax.vmap, jax.tree_utils,...

Privitives

Common primitives (Ray, Camera, Transform,...), so user can express intent, instead of math.

H, W = (256, 1024)
cam_spec = v3d.PinholeCamera.from_focal(
    resolution=(H, W),
    focal_in_px=35.,
)
cam = v3d.Camera.from_look_at(
    spec=cam_spec,
    pos=[5, 5, 5],
    target=[0, 0, 0],  # Camera looks at the scene center
)

rays = cam.rays()  # Rays in world coordinates

# Project `(*shape, 3)` world coordinates into `(*shape, 2)` pixel coordinates.
px_coords = cam.px_from_world @ point_cloud

See the API for a full list of primitive.

Creating your own primitives is trivial.

Converting any dataclass to dataclass array is trivial:

Inherit from v3d.DataclassArray
Use etils.array_types to annotate the array fields (or exlicitly use my_field: Any = dca.field(shape=, dtype=) instead of dataclasses.field)

from etils.array_types import FloatArray


class MyRay(v3d.DataclassArray):
  pos: FloatArray[..., 3]
  dir: FloatArray[..., 3]


rays = MyRay(pos=jnp.zeros((H, W, 3)), dir=jnp.ones((H, W, 3)))
assert rays.shape == (H, W)

v3d makes it easy to opt-in to the feature you need by implementing the corresponding protocol.

Documentation

The best way to get started is to try the Colab tutorials (in the documentation):

Installation:

pip install visu3d

Usage:

import visu3d as v3d

Installation

pip install visu3d

This is not an official Google product.

google-research/visu3d

visu3d - 3D geometry made easy

Core features

Privitives

Documentation

Installation