Learning Generic Local Shape Properties for Adaptive Super-Sampling

Christian Reinbold, R. Westermann
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Abstract

We propose a novel encoder/decoder-based neural network architecture that learns view-dependent shape and appearance of geometry represented by voxel representations. Since the network is trained on local geometry patches, it generalizes to arbitrary models. A geometry model is first encoded into a sparse voxel octree of features learned by a network, and this model representation can then be decoded by another network in-turn for the intended task. We utilize the network for adaptive super-sampling in ray-tracing, to predict super-sampling patterns when seeing coarse-scale geometry. We discuss and evaluate the proposed network design, and demonstrate that the decoder network is compact and can be integrated seamlessly into on-chip ray-tracing kernels. We compare the results to previous screen-space super-sampling strategies as well as non-network-based world-space approaches.
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自适应超采样的一般局部形状特性学习
我们提出了一种新的基于编码器/解码器的神经网络架构,它可以学习由体素表示的几何形状和外观。由于网络是在局部几何块上训练的,因此它可以推广到任意模型。首先将几何模型编码为网络学习到的稀疏体素八叉树特征,然后该模型表示可以由另一个网络依次解码以完成预期的任务。我们利用该网络在光线追踪中进行自适应超采样,在看到粗尺度几何时预测超采样模式。我们讨论并评估了所提出的网络设计,并证明了解码器网络是紧凑的,可以无缝集成到片上光线跟踪内核中。我们将结果与之前的屏幕空间超级采样策略以及非基于网络的世界空间方法进行了比较。
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