Stratified sampling is the ray-sampling strategy that divides an interval into bins and draws samples within each bin to reduce estimator variance - it improves coverage and training stability in volumetric rendering.

What Is Stratified sampling?

• Definition: Ray segments are partitioned and sampled with jitter to avoid clustering artifacts.
• Variance Control: Even sample distribution lowers Monte Carlo variance compared with naive random sampling.
• NeRF Use: Common in coarse rendering passes during training and inference.
• Deterministic Mode: Can switch to fixed bin centers for reproducible evaluation.

Why Stratified sampling Matters

• Gradient Quality: More uniform ray coverage improves optimization signal consistency.
• Artifact Reduction: Helps prevent missed thin structures and noisy opacity estimates.
• Efficiency: Provides strong baseline quality without complex adaptive logic.
• Theoretical Soundness: Well-understood estimator behavior makes tuning easier.
• Pipeline Compatibility: Works well with hierarchical resampling and importance sampling steps.

How It Is Used in Practice

• Bin Count: Tune sample count per ray based on scene complexity and latency budget.
• Jitter Policy: Use randomized jitter in training and deterministic sampling for benchmarks.
• Hybrid Setup: Pair stratified coarse pass with fine importance pass for best tradeoff.

Stratified sampling is a standard low-variance sampling technique in NeRF pipelines - stratified sampling remains a reliable default when balancing rendering quality and computational cost.