SpatialEvo: Self-Evolving Spatial Intelligence via Deterministic Geometric Environments

1. 1
   Design Your Deterministic Geometric Environment

   Programmatically define a virtual 3D space where all geometric properties (object shapes, positions, materials, lighting) are precisely known and controllable. This allows for exact calculation of ground truth.
2. 2
   Automate Data and Ground Truth Generation

   Develop scripts to render diverse synthetic scenes from your environment, automatically extracting high-fidelity ground truth labels (e.g., depth maps, semantic segmentation, object poses) for each scene without manual effort.
3. 3
   Train Your Spatial Reasoning Model

   Use the automatically generated synthetic data and ground truth to train or fine-tune your 3D scene reasoning model. Focus on tasks like object detection, segmentation, or depth estimation within the environment.
4. 4
   Implement Self-Evolution Logic

   Design a mechanism where the model's performance (e.g., on novel synthetic scenes or specific failure modes) informs how new training data is generated or how the environment evolves to challenge the model further, creating a closed-loop system.
5. 5
   Validate and Iterate

   Periodically evaluate the model's performance on more complex synthetic scenarios or real-world data. Use these insights to refine the environment generation, data diversity, and model architecture, driving continuous improvement.

import numpy as np

class DeterministicScene:
    def __init__(self):
        self.objects = []
        self.camera_pose = np.eye(4) # Example: identity matrix for camera

    def add_cube(self, position, size, color):
        # In a real system, this would add a renderable 3D object to a scene graph
        self.objects.append({
            'type': 'cube',
            'position': np.array(position),
            'size': np.array(size),
            'color': color
        })
        print(f"Added cube at {position} with size {size} and color {color}")

    def get_ground_truth_depth(self):
        # Placeholder: In a real system, this would render depth from camera_pose
        print("Generating ground truth depth map...")
        return np.zeros((100, 100)) # Dummy depth map for illustration

    def get_ground_truth_segmentation(self):
        # Placeholder: Render segmentation
        print("Generating ground truth segmentation map...")
        return np.zeros((100, 100), dtype=int) # Dummy segmentation map

# Example Usage:
my_scene = DeterministicScene()
my_scene.add_cube(position=[0, 0, 5], size=[1, 1, 1], color=[1.0, 0.0, 0.0])
my_scene.add_cube(position=[1, 2, 6], size=[0.5, 0.5, 0.5], color=[0.0, 1.0, 0.0])
depth_map = my_scene.get_ground_truth_depth()
segmentation_map = my_scene.get_ground_truth_segmentation()
print("Scene setup complete. Ground truth generation initiated.")