Vaibhav Rathod's Notes

PathTracer Learning - Concept - Temporal Rejection

2 min read

  • Why Rejection is Needed

    • Temporal accumulation assumes previous frame’s sample is valid for current pixel
    • This assumption breaks when:
      • Disocclusion: object moves to reveal previously hidden surface
      • Fast motion: reprojection lands outside the frame or on wrong surface
      • Lighting changes: sudden light on/off
      • Material changes: texture swap, animation
    • Without rejection: ghosting artifacts (stale samples persist)

  • Depth-Based Rejection

    • Compare depth of reprojected pixel vs expected depth
    • prev_depth = sample(prev_depth_buffer, prev_uv)
    • expected_depth = reproject(current_depth, camera_motion)
    • If |prev_depth - expected_depth| > threshold: reject
    • Threshold: typically 0.1 * current_depth (relative threshold)
    • Catches disocclusion and large depth discontinuities

  • Normal-Based Rejection

    • Compare surface normals between frames
    • prev_normal = sample(prev_normal_buffer, prev_uv)
    • If dot(current_normal, prev_normal) < threshold: reject
    • Threshold: typically cos(25°) ≈ 0.9
    • Catches surfaces that rotated significantly (e.g., spinning objects)

  • Color-Based Rejection (Variance Clipping)

    • Clamp previous sample to neighborhood color range
    • Compute mean μ and variance σ² of current pixel’s 3×3 neighborhood
    • Clip previous sample to [μ - k*σ, μ + k*σ] (AABB in color space)
    • k = 1.0 is typical
    • Doesn’t fully reject — blends toward valid range
    • Used in TAA (Temporal Anti-Aliasing) — very effective for ghosting

  • Velocity-Based Rejection

    • Large motion vectors indicate fast-moving objects
    • If length(motion_vector) > threshold: increase blend factor (more current frame)
    • Doesn’t fully reject but reduces ghosting for fast motion

  • Disocclusion Detection

    • Most challenging case — no previous sample exists for newly visible pixels
    • Detection: reprojected UV is outside [0,1] range → definitely disoccluded
    • Depth test catches most in-frame disocclusions
    • For remaining cases: use stencil buffer or object ID comparison

  • Rejection Response

    • Full rejection: α = 1.0 — use only current frame (very noisy)
    • Partial rejection: increase α smoothly based on confidence
    • Confidence map: per-pixel value in [0,1] indicating temporal reliability
    • α = lerp(α_min, 1.0, 1.0 - confidence)

Graph View

Backlinks