Vaibhav Rathod's Notes

PathTracer Learning - ReSTIR

3 min read

  • ReSTIR — Reservoir-based Spatiotemporal Importance Resampling

    • Bitterli et al. 2020 — one of the most impactful real-time rendering papers.
    • Enables high-quality direct lighting with thousands of lights at real-time rates.
    • Parent: PathTracer Learning

  • The Problem ReSTIR Solves

    • Direct lighting with many lights
      • Naive: sample one random light per pixel → high variance with many lights
      • Better: sample proportional to light contribution → need to evaluate all lights
      • ReSTIR: efficiently find good light samples by resampling candidates
    • Key insight
      • Resampled Importance Sampling (RIS): generate M candidates, select 1 proportional to target PDF
      • Reservoir: compact data structure to maintain RIS state
      • Temporal reuse: reuse reservoir from previous frame (free extra samples)
      • Spatial reuse: share reservoirs with neighbors (more free samples)

  • Resampled Importance Sampling (RIS)

    • Goal: sample from target distribution p̂(x) when direct sampling is hard
    • Algorithm
      • Generate M candidates {x_1, ..., x_M} from source distribution q(x)
      • Select one candidate with probability proportional to w_i = p̂(x_i) / q(x_i)
      • The selected sample approximates sampling from p̂
    • Weight formula
      • W = (1/M) * Σ w_i — sum of weights
      • Unbiased contribution weight: W_selected = W / p̂(x_selected)
    • In practice for direct lighting
      • q(x) = uniform over all lights (easy to sample)
      • p̂(x) = L_e(x) * G(x) * V(x) — emission × geometry × visibility
      • Visibility V(x) is expensive — defer to final evaluation

  • The Reservoir Data Structure

    • Compact structure per pixel
    • Streaming update (one candidate at a time)
    • Merging two reservoirs

  • ReSTIR DI Algorithm (per frame)

    • Pass 1: Initial candidates
      • For each pixel: sample M=32 light candidates from q (uniform)
      • Store in reservoir
    • Pass 2: Temporal reuse
      • Reproject previous frame’s reservoir to current pixel
      • Merge current reservoir with reprojected reservoir
      • Cap M to prevent unbounded growth (M_max = 20 typically)
    • Pass 3: Spatial reuse
      • For each pixel: sample K=5 neighboring pixels
      • Merge their reservoirs into current pixel’s reservoir
      • Apply MIS weights to avoid bias from different pixel domains
    • Pass 4: Shading
      • Evaluate visibility for final selected sample (one shadow ray)
      • Compute shading: L = f_r * L_e * G * V * W

  • Bias in ReSTIR

    • Naive spatial reuse is biased
      • Neighbor’s reservoir was selected for neighbor’s domain, not current pixel
      • Using it directly introduces bias (incorrect PDF)
    • Correction methods
      • MIS-based resampling (Talbot 2005) — correct but expensive
      • Pairwise MIS (Bitterli 2022) — efficient and unbiased
      • 1/M heuristic — biased but fast, often acceptable

  • ReSTIR GI (Global Illumination)

    • Extends ReSTIR to indirect lighting
    • Each reservoir stores a full path segment (not just a light)
    • Temporal and spatial reuse of path segments
    • Much more complex bias correction needed
    • Paper: “ReSTIR GI: Path Resampling for Real-Time Path Tracing” (Ouyang et al. 2021)

  • Implementation Notes

    • Two reservoir buffers needed (ping-pong for temporal)
    • Spatial reuse needs neighbor reservoirs from same frame (not current pass)
    • Use a separate spatial pass reading from temporal output
    • Visibility reuse: cache shadow ray results to avoid redundant traces

Graph View

Backlinks