Given a 3D mesh or a robot URDF, create a "ballpark" estimate of its spherical collision geometry.

Features include:

• Fast mesh-to-sphere decomposition via recursive PCA-based splitting.
• Sphere set optimization with volume-based losses, and more.
• Different presets for conservative, balanced, or surface-fitting sphere sets.

For robot URDFs, we also include:

• Automatic sphere distribution across robot links, proportional to their geometry complexity.
• Spheres are optimized on a robot-level to have minimal self-collision distance at rest pose.
• Similar links are detected and share sphere parameters for visual and geometric consistency.
• JSON export with sphere parameters for each link, and an ignore-list of link pairs for collision checking.

We also include a set of interactive visualization tools, supported via viser.

pip install -e .
pip install -e ".[examples]"
pip install -e ".[dev]"  # with development tools (linting, testing)

import trimesh
from ballpark import spherize

# Load mesh
mesh = trimesh.load("object.stl")

# Generate spheres with adaptive fitting
spheres = spherize(mesh, target_spheres=32)

for s in spheres:
    print(f"center={s.center}, radius={s.radius}")

import yourdfpy
from robot_descriptions.loaders.yourdfpy import load_robot_description
from ballpark import Robot, BallparkConfig, SpherePreset

# Load robot URDF with collision meshes
urdf = load_robot_description("panda_description")
urdf_coll = yourdfpy.URDF(
    robot=urdf.robot,
    load_collision_meshes=True,
)

# Create robot and generate spheres
robot = Robot(urdf_coll)
result = robot.spherize(target_spheres=100)

# Optional: refine with link- and robot-level costs
config = BallparkConfig.from_preset(SpherePreset.BALANCED)
result = robot.refine(result, config=config)

for link_name, spheres in result.link_spheres.items():
    print(f"{link_name}: {len(spheres)} spheres")

We include an interactive script for adjusting the pose + radii of the auto-generated spheres.

This project builds on ideas from:

• foam
• MorphIt