大家好，本课是本次专题课的最后一节课，给出了未来的研究改进方向，谢谢！

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未来的研究改进方向

1.等待WebNN Polyfill 支持WebGPU后端

2.使用WebGPU的GPU Buffer或者GPU Texture作为Network的输入和输出

参考资料：
https://www.w3.org/TR/webnn/#programming-model-device-selection

https://www.w3.org/TR/webnn/#api-ml

https://www.w3.org/TR/webnn/#api-mlcontext-webgpu-interop

https://www.w3.org/TR/webnn/#api-mlcommandencoder

Create tensor from GPUBuffer

3.与path tracer结合

4.使用多帧来累积spp(temporally accumulating)

对于训练，加入累积spp的数据（参考wspk的dataset）

对于推理，使用累积spp的场景数据作为输入

参考资料：

• wspk论文的相关描述：

Besides, temporally accumulating consecutive 1-spp frames can effectively improve the temporal stability and increase the effec- tive spp of each frame. We employ a temporal accumulation pre- processing step before sending the noisy inputs to the denoising pipeline just like [SKW∗ 17, KIM∗ 19, MZV∗ 20]. We first reproject the previous frame to the current frame with the motion vector and then judge their geometry consistency by world position and shad- ing normal feature buffers. Current frame pixels that passed the consistency test are blended with their corresponding pixels in the previous frame, while the failed pixels remain original 1 spp.

• wspk的相关实现
• bmfr的相关实现

对于Motion Vector造成的ghost问题，可参考下面的论文改进：Temporally Reliable Motion Vectors for Real-time Ray Tracing