depth completion from sparse lidar

**Depth completion from sparse lidar** is the **task of generating dense depth maps by combining sparse lidar points with image context and learned geometric priors** - it converts low-density range sampling into full-resolution scene depth. **What Is Depth Completion?** - **Definition**: Predict dense per-pixel depth using sparse depth measurements as anchors. - **Input Sources**: Sparse lidar projection plus RGB image or image features. - **Primary Challenge**: Fill large missing regions without hallucinating inconsistent geometry. - **Output Use**: Autonomous driving perception, mapping, and 3D understanding. **Why Sparse-to-Dense Completion Matters** - **Sensor Efficiency**: Maximizes utility of low-cost or low-line-count lidar. - **Metric Accuracy**: Sparse points provide absolute depth anchors for scale. - **Perception Quality**: Dense depth improves obstacle boundaries and scene interpretation. - **Fusion Utility**: Bridges camera detail with lidar reliability. - **Deployment Value**: Essential in automotive and robotics stacks. **Completion Approaches** **Guided CNN Fusion**: - Concatenate sparse depth and RGB features. - Predict dense depth with confidence-aware refinement. **Spatial Propagation Networks**: - Propagate sparse measurements to neighbors with learned affinity. - Preserve edges and discontinuities. **Transformer Fusion Models**: - Use cross-attention between sparse depth tokens and dense image tokens. - Improve long-range completion consistency. **How It Works** **Step 1**: - Project lidar points to image plane and encode sparse depth plus RGB context. **Step 2**: - Predict dense depth and refine with edge-aware and anchor consistency losses. Depth completion from sparse lidar is **a critical fusion task that turns sparse geometric anchors into full-resolution, metric-consistent depth maps** - it is a core component of practical 3D perception pipelines.