Adaptive depth bias for shadow maps

  • Authors:
  • Hang Dou;Yajie Yan;Ethan Kerzner;Zeng Dai;Chris Wyman

  • Affiliations:
  • The University of Iowa;The University of Iowa;SCI Institute;The University of Iowa;NVIDIA

  • Venue:
  • Proceedings of the 18th meeting of the ACM SIGGRAPH Symposium on Interactive 3D Graphics and Games
  • Year:
  • 2014

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Abstract

Shadow aliasing due to limited storage precision has been plaguing discrete shadowing algorithms for decades. We present a simple method to eliminate false self-shadowing through adaptive depth bias. Unlike existing methods which simply set the weight of the bias based on surface slope or utilize the second nearest surface, we evaluate the bound of bias for each fragment and compute the optimal bias within the bound. Our method introduces small overhead, preserves more shadow details than widely used constant bias and slope scale bias and works for common 2D shadow maps as well as 3D binary shadow volumes.