Temporal Coherence-based Distributed Ray Tracing of Massive Scenes

Distributed ray tracing algorithms are widely used when rendering massive scenes, where data utilization and load balancing are the keys to improving performance. One essential observation is that rays are temporally coherent, which indicates that temporal information can be used to improve computational efficiency. In this paper, we use temporal coherence to optimize the performance of distributed ray tracing. First, we propose a temporal coherence-based scheduling algorithm to guide the task/data assignment and scheduling. Then, we propose a virtual portal structure to predict the radiance of rays based on the previous frame, and send the rays with low radiance to a precomputed simplified model for further tracing, which can dramatically reduce the traversal complexity and the overhead of network data transmission. The approach was validated on scenes of sizes up to 355 GB. Our algorithm can achieve a speedup of up to 81% compared to previous algorithms, with a very small mean squared error.

    author          = {Xu, Xiang and Wang, Lu and Pérard-Gayot, Arsène and Membarth, Richard and Li, Cuiyu and Yang, Chenglei and Slusallek, Philipp},
    title           = {Temporal Coherence-Based Distributed Ray Tracing of Massive Scenes},
    journal         = {IEEE Transactions on Visualization and Computer Graphics (TVCG)},
    pages           = {1489--1501},
    volume          = {30},
    number          = {2},
    year            = 2022,
    month           = nov,
    date            = {2022-11-07},
    doi             = {10.1109/TVCG.2022.3219982},
    publisher       = {IEEE}