Efficient Progressive Radiance Estimation Engine Architecture and Implementation for Progressive Photon Mapping

We propose a progressive radiance estimation engine (PREE) hardware architecture to accelerate the processing of the progressive photon mapping with satisfactory graphic quality. The presented PREE architecture consists of four progressive radiance estimation units (PREUs), approximate full task schedule-oriented hit-point update operation controller (AFTSO-HpUOC) and approximate data-independent schedule-oriented radiance evaluation controller (ADISO-REC). The PREUs accelerate the radiance estimation computation by a pipeline technique and share and configure the hardware resource for hit-point update operation and radiance evaluation. Through AFTSO-HpUOC and ADISO-REC, the data can be efficiently dispatched to achieve better parallelism and the data dependence can be alleviated within the four PREUs, respectively. The core area of the proposed PREE architecture implemented in TSMC 90-nm CMOS process is 1.78 mm2. According to the post-layout simulation results, the implementation achieves 496.79 million hit-point update operations per second (MHpUO/s) and consumes 184 mW at 125 MHz for Cornell box with three balls.

[1]  Theo Ungerer,et al.  Processor architecture - from dataflow to superscalar and beyond , 1999 .

[2]  Milos D. Ercegovac,et al.  Accelerating the photon mapping algorithm and its hardware implementation , 2011, ASAP 2011 - 22nd IEEE International Conference on Application-specific Systems, Architectures and Processors.

[3]  Seung hyun Pan A Multi-Accelerator Architecture for Photon Mapping , 2014 .

[4]  Toshiya Hachisuka,et al.  Parallel progressive photon mapping on GPUs , 2010, SIGGRAPH ASIA.

[5]  Petros Faloutsos,et al.  The photon pipeline revisited , 2007, The Visual Computer.

[6]  Yangdong Deng,et al.  FastTree: A hardware KD-tree construction acceleration engine for real-time ray tracing , 2015, 2015 Design, Automation & Test in Europe Conference & Exhibition (DATE).

[7]  H. Jensen,et al.  A progressive error estimation framework for photon density estimation , 2010, SIGGRAPH 2010.

[8]  Toshiya Hachisuka Implementing a Photorealistic Rendering System using GLSL , 2015, ArXiv.

[9]  Shawn Singh The photon pipeline , 2006, GRAPHITE '06.

[10]  Yang Wang,et al.  A Hardware Acceleration Engine for Ray Tracing , 2014, 2014 IEEE 17th International Conference on Computational Science and Engineering.

[11]  Eero P. Simoncelli,et al.  Image quality assessment: from error visibility to structural similarity , 2004, IEEE Transactions on Image Processing.

[12]  H. Jensen,et al.  Progressive photon mapping , 2008, SIGGRAPH 2008.

[13]  H. Jensen,et al.  Stochastic progressive photon mapping , 2009, ACM Trans. Graph..

[14]  Hans-Peter Seidel,et al.  Fast Final Gathering via Reverse Photon Mapping , 2005, Comput. Graph. Forum.

[15]  Stian Aaraas Pedersen Progressive Photon Mapping on GPUs , 2013 .

[16]  Matthias Zwicker,et al.  Progressive photon mapping: A probabilistic approach , 2011, TOGS.

[17]  Anton Kaplanyan,et al.  Adaptive progressive photon mapping , 2013, TOGS.

[18]  Henrik Wann Jensen,et al.  Global Illumination using Photon Maps , 1996, Rendering Techniques.

[19]  Pat Hanrahan,et al.  Photon mapping on programmable graphics hardware , 2003, HWWS '03.

[20]  Xin Sun,et al.  T-ReX: Interactive Global Illumination of Massive Models on Heterogeneous Computing Resources , 2014, IEEE Transactions on Visualization and Computer Graphics.

[21]  Morgan McGuire,et al.  Hardware-accelerated global illumination by image space photon mapping , 2009, High Performance Graphics.

[22]  Xiangxu Meng,et al.  A survey of photon mapping state-of-the-art research and future challenges , 2016, Frontiers of Information Technology & Electronic Engineering.