前一陣子微軟講過雲端能夠輔助畫面，被很多人斥為是誇大其辭
不過Nvidia就新發表了一套藉雲端運算的間接光源來輔助即時光影渲染的技術CloudLight
影片:
http://youtu.be/4GgQ0pfNGyc
資訊:
http://www.allgamesbeta.com/2013/07/nvidia-unveils-cloud-based-indirect.html
CloudLight: A system for amortizing indirect lighting in real-time rendering
We introduce CloudLight, a system for computing indirect lighting in the
Cloud to support real-time rendering for interactive 3D applications on a
user's local device.
CloudLight maps the traditional graphics pipeline onto a distributed system.
That differs from a single-machine renderer in three fundamental ways. First,
the mapping introduces potential asymmetry between computational resources
available at the Cloud and local device sides of the pipeline. Second,
compared to a hardware memory bus, the network introduces relatively large
latency and low bandwidth between certain pipeline stages. Third, for
multi-user virtual environments, a Cloud solution can amortize expensive
global illumination costs across users.
Our new CloudLight framework explores tradeoffs in different partitions of
the global illumination workload between Cloud and local devices, with an eye
to how available network and computational power influence design decisions
and image quality. We describe the tradeoffs and characteristics of mapping
three known lighting algorithms to our system and demonstrate scaling for up
to 50 simultaneous CloudLight users.
--
不過Nvidia就新發表了一套藉雲端運算的間接光源來輔助即時光影渲染的技術CloudLight
影片:
http://youtu.be/4GgQ0pfNGyc
資訊:
http://www.allgamesbeta.com/2013/07/nvidia-unveils-cloud-based-indirect.html
CloudLight: A system for amortizing indirect lighting in real-time rendering
We introduce CloudLight, a system for computing indirect lighting in the
Cloud to support real-time rendering for interactive 3D applications on a
user's local device.
CloudLight maps the traditional graphics pipeline onto a distributed system.
That differs from a single-machine renderer in three fundamental ways. First,
the mapping introduces potential asymmetry between computational resources
available at the Cloud and local device sides of the pipeline. Second,
compared to a hardware memory bus, the network introduces relatively large
latency and low bandwidth between certain pipeline stages. Third, for
multi-user virtual environments, a Cloud solution can amortize expensive
global illumination costs across users.
Our new CloudLight framework explores tradeoffs in different partitions of
the global illumination workload between Cloud and local devices, with an eye
to how available network and computational power influence design decisions
and image quality. We describe the tradeoffs and characteristics of mapping
three known lighting algorithms to our system and demonstrate scaling for up
to 50 simultaneous CloudLight users.
--
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