depth fusion
**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.