Multi-View Semantic Fusion Score

The problem

It is difficult to measure how well different 3D perspectives of an object align with a specific task or instruction. This makes it hard to know if a system truly understands the relationship between visual data and human goals.

What it does

It calculates a single score that measures the semantic alignment between multiple 3D views and a specific task. It combines logic from two different approaches to provide a more accurate measurement.

Why it matters

It provides a clear way to quantify how well 3D data matches human instructions.

Validation

It was run in the sandbox and it failed. run produced no meaningful output (empty or near-empty).

$ python3 fusion_score.py
{"score": 2.3449999999999998}

No screenshot — there is nothing working to show. This is recorded as an unfinished sketch so the attempt stays visible instead of being quietly dropped.

The code

All of it — 45 lines, one file, standard library only.

import json

class MultiViewFusion:
    def __init__(self, views):
        self.views = views  # List of tuples containing (2d_features, 3d_geometry)
        self.fusion_weights = [0.4, 0.6]  # Weighting for cross-modal fusion
        self.diversity_matrix = None

    def _cross_modal_fusion(self):
        """Inst3D-LMM inspired MCMF logic"""
        # Simulate cross-modal fusion with learned weights
        fused_features = []
        for view in self.views:
            fused = self.fusion_weights[0] * view[0] + self.fusion_weights[1] * view[1]
            fused_features.append(fused)
        return fused_features

    def _instruction_guided_diversity(self, instructions):
        """MultiInstruct inspired diversity mapping"""
        # Calculate diversity score based on instruction embeddings
        diversity_scores = []
        for inst in instructions:
            # Simulate embedding and diversity calculation
            div_score = len(inst.split()) * 0.5 # Simple placeholder calculation
            diversity_scores.append(div_score)
        return diversity_scores

    def calculate_fusion_score(self, instructions):
        """Main scoring function"""
        fused_features = self._cross_modal_fusion()
        diversity_scores = self._instruction_guided_diversity(instructions)
        
        # Combine fusion and diversity scores
        final_score = 0.7 * sum(fused_features) / len(fused_features) + 0.3 * sum(diversity_scores) / len(diversity_scores) if fused_features and diversity_scores else 0.0
        return final_score

# Example usage
if __name__ == "__main__":
    # Simulated input data
    views = [ (1.0, 2.0), (3.0, 4.0) ]
    instructions = ["Describe shape and color", "Analyze spatial relationships"]

    scorer = MultiViewFusion(views)
    score = scorer.calculate_fusion_score(instructions)
    print(json.dumps({"score": score}))