vizion3d 1.3.4

U3D Capability and Intelligence for the real world.

vizion3D

vizion3d is an open-source Python library for 3D computer vision — a single unified interface for depth estimation, point cloud generation, 3D object annotation, and more.

Point-cloud inputs and outputs use OpenGL/viewer camera space: X+ right, Y+ up, and Z- forward into the scene.

📖 Full documentation →

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Installation

Requires Python 3.12 (Open3D constraint).

pip install "vizion3d[cpu]"

For NVIDIA CUDA, AMD ROCm, or Apple Silicon MPS, see the Hardware Acceleration page.

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Quick start — depth estimation

import open3d as o3d
from vizion3d.lifting import DepthEstimation, DepthEstimationCommand

result = DepthEstimation().run(
    DepthEstimationCommand(
        image_input="roomhd.jpg",
        return_depth_image=True,
        return_point_cloud=True,
    )
)

print(f"Depth range : {result.min_depth:.4f} → {result.max_depth:.4f}")
print(f"Points      : {len(result.point_cloud.points)}")
print(f"Scale       : {result.point_cloud_scale} metre per unit")

o3d.io.write_point_cloud("roomhd_result.ply", result.point_cloud)

The saved PLY uses OpenGL/viewer camera space: X+ right, Y+ up, Z- forward.

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Quick start — 3D object annotation

import open3d as o3d
from vizion3d.annotation import ObjectMaskAnnotation3D, ObjectMaskAnnotation3DCommand

pcd = o3d.io.read_point_cloud("roomhd_result.ply")

result = ObjectMaskAnnotation3D().run(
    ObjectMaskAnnotation3DCommand(
        point_cloud=pcd,
        image_input="roomhd.jpg",
        return_annotated_cloud=True,
    )
)

for ann in result.annotations:
    print(f"{ann.label:20s}  conf={ann.confidence:.2f}  3D points={len(ann.point_indices)}")

o3d.io.write_point_cloud("annotated.ply", result.annotated_cloud)

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Documentation

Full reference, REST/gRPC guides, hardware setup, and task catalogue:

https://olafenwamoses.github.io/vizion3D/