Depth fusion is the process of combining depth estimates from multiple sensors or algorithms into a single more accurate and robust depth representation - fusion exploits complementary strengths while reducing modality-specific errors.

What Is Depth Fusion?

• Definition: Weighted integration of depth sources such as stereo, ToF, lidar, and monocular predictors.
• Fusion Objective: Improve coverage, precision, and reliability over any individual source.
• Input Differences: Each modality has distinct noise patterns and range characteristics.
• Output Form: Unified depth map and often per-pixel confidence.

Why Depth Fusion Matters

• Robustness: Handles sensor failure modes and environmental challenges better.
• Accuracy Gain: Combines metric anchors with dense structural detail.
• Coverage Improvement: Fills holes where one modality is weak.
• Reliability for Control: Better depth confidence improves planning safety.
• System Flexibility: Supports heterogeneous sensor suites in robotics and automotive.

Fusion Methods

Probabilistic Fusion:

• Combine depth with uncertainty weighting.
• Bayesian or Kalman-style updates per pixel or region.

Learned Fusion Networks:

• Neural models learn modality weighting and residual correction.
• Adapt to scene context and sensor noise.

Geometric Consistency Fusion:

• Enforce multi-view constraints while merging depth cues.
• Reduce outliers and preserve edges.

How It Works

Step 1:

• Align depth sources into common frame and estimate per-source confidence.

Step 2:

• Fuse depths using probabilistic or learned weighting and refine with consistency constraints.

Depth fusion is the reliability amplifier for 3D perception that combines multiple imperfect depth sources into one stronger estimate - confidence-aware fusion is the key to stable downstream autonomy behavior.