Crowd-Sourced Video Rendering via Distributed Path Tracing
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Solution Overview
Problem
Traditional server-side graphics rendering faces scalability issues due to high costs and underutilization of resources, as it requires additional GPUs or servers to handle increasing client demands, and existing multi-GPU rendering methods suffer from poor scalability and high latency.
Innovation Solution
A crowd-sourced graphics rendering system that distributes rendering tasks among connected clients with capable GPUs, using path tracing to render diffuse lighting and transmitting render assets in the form of viewport state data objects (VSDOs), allowing clients to contribute to the rendering process and generate a composite output for all clients.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If traditional server-side graphics rendering uses additional GPUs or servers to handle increasing client demands, then rendering quality and speed are improved, but system cost and device complexity increase significantly
Solution Approach 1:
The patent segments the rendering workload by dividing the scene into multiple renderable regions or tasks that can be distributed to different client devices. Each client renders a portion of the overall scene, and the server composites these portions into the final rendered output. This segmentation allows the system to leverage multiple client GPUs instead of requiring a single powerful server with extensive GPU infrastructure.
2Manufacturing precision
If traditional server-side graphics rendering adds more GPUs to meet increasing client demands, then rendering quality is improved, but resource underutilization occurs when demand falls below capacity
Solution Approach 1:
The patent implements a dynamic rendering system where the server adapts the rendering workload distribution based on real-time client availability and demand. When more clients are available, more rendering tasks are distributed to them, improving quality and utilizing available resources. When fewer clients are available, the server adjusts accordingly, preventing resource underutilization. This dynamic approach allows the system to optimize both rendering quality and resource efficiency based on current system state.
3Productivity
If existing multi-GPU rendering methods are used on the server, then rendering speed is improved, but scalability is poor and latency increases
Solution Approach 1:
The patent introduces a task distribution and compositing intermediary layer between the server and clients. The server acts as an intermediary that distributes rendering tasks to multiple clients, collects their rendered outputs, and composites them into the final scene. This intermediary approach enables parallel rendering across multiple devices, improving overall rendering speed while maintaining low latency through efficient task management and synchronization.
Data Source
AI summary
In one embodiment, a method includes distributing rendering tasks to connected client nodes having capable graphics processing units by transmitting viewport state data objects and a unique spatial location to each of the clients, performing path tracing at each of the clients from the starting point of their unique spatial locations, and transmitting their rendered output back to the server. The server generates a composite rendered output from the individual rendered outputs received by the participating clients, and then transmits the composite to all connected clients for display. Thus, as the number of client nodes increases, the scene is rendered more rapidly and at higher quality. In particular embodiments, the rendered output is a lightmap representing the diffuse lighting for the scene, and each client may render the scene's specular highlights from its own viewport.