多分辨率哈希编码的即时神经图形基元
Instant Neural Graphics Primitives with a Multiresolution Hash Encoding
摘要：
由完全连接的神经网络参数化的神经图形基元的训练和评估可能是昂贵的。我们降低了这种成本，一个多功能的新的输入编码，允许使用一个较小的网络，而不牺牲质量，从而显着减少浮点数和内存访问操作的数量：一个小的神经网络是由一个多分辨率哈希表的可训练的特征向量，其值通过随机梯度下降优化。多分辨率结构允许网络消除哈希冲突的歧义，从而形成一个简单的架构，在现代GPU上并行化是微不足道的。我们通过使用完全融合的CUDA内核实现整个系统来利用这种并行性，重点是最大限度地减少浪费的带宽和计算操作。我们实现了几个数量级的综合加速，能够在几秒钟内训练高质量的神经图形基元，并在几十毫秒内以${1920\!\times\!1080}$的分辨率进行渲染。
英文摘要：
Neural graphics primitives, parameterized by fully connected neural networks, can be costly to train and evaluate. We reduce this cost with a versatile new input encoding that permits the use of a smaller network without sacrificing quality, thus significantly reducing the number of floating point and memory access operations: a small neural network is augmented by a multiresolution hash table of trainable feature vectors whose values are optimized through stochastic gradient descent. The multiresolution structure allows the network to disambiguate hash collisions, making for a simple architecture that is trivial to parallelize on modern GPUs. We leverage this parallelism by implementing the whole system using fully-fused CUDA kernels with a focus on minimizing wasted bandwidth and compute operations. We achieve a combined speedup of several orders of magnitude, enabling training of high-quality neural graphics primitives in a matter of seconds, and rendering in tens of milliseconds at a resolution of ${1920\!\times\!1080}$.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
