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.