GRID Docs | General Robotics

Estimate depth from stereo pair using Fast-FoundationStereo.

Parameters

Union[str, Image.Image, np.ndarray]

required

Left stereo image.

Union[str, Image.Image, np.ndarray]

required

Right stereo image.

Dict[str, Any]

Camera parameters: - “K”: 3x3 camera intrinsics matrix - “baseline”: Stereo baseline in meters - “hiera”: Use hierarchical coarse-to-fine inference (bool) - “valid_iters”: Number of GRU refinement iterations (default 8)

float | None

Optional HTTP timeout.

Returns

np.ndarray: Depth map as numpy array (H, W) with dtype float32. Values represent metric depth in meters.

Example

from grid_cortex_client import CortexClient, ModelType
import numpy as np
from PIL import Image
client = CortexClient()
K = np.array([[525, 0, 320], [0, 525, 240], [0, 0, 1]], dtype=np.float32)
aux = {"K": K, "baseline": 0.1, "hiera": False, "valid_iters": 8}
left_image = np.array(Image.open("left.jpg"))
right_image = np.array(Image.open("right.jpg"))
depth = client.run(ModelType.FAST_FOUNDATIONSTEREO, left_image=left_image, right_image=right_image, aux_args=aux)
print(depth.shape)  # (480, 640)

Fast-FoundationStereo is ~10x faster than the original FoundationStereo while maintaining comparable accuracy (14.6M params, real-time capable).

Overview

Cortex Models

fast-foundationstereo

Parameters

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Example

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Example