CoordX：使用拆分MLP架构加速隐式神经表示
CoordX: Accelerating Implicit Neural Representation with a Split MLP Architecture
摘要：
隐式神经表示与多层感知器（MLP）最近获得了突出的各种各样的任务，如新的视图合成和3D对象表示和渲染。然而，这些表示的一个重大挑战是，在大量输入坐标上使用MLP进行训练和推理以学习和表示图像，视频或3D对象，需要大量计算并导致长处理时间。在这项工作中，我们的目标是通过提出一种新的分裂MLP架构CoordX来加速隐式神经表示的基于坐标的MLP的推理和训练。使用CoordX，初始层被拆分以分别学习输入坐标的每个维度。然后通过最后一层融合中间特征，以在相应的坐标点处生成学习信号。这大大减少了所需的计算量，并导致训练和推理的大幅加速，同时实现了与基线MLP相似的精度。因此，该方法旨在首先学习作为原始信号的分解的函数，然后融合它们以生成学习信号。我们提出的架构可以普遍用于许多隐式神经表征任务，没有额外的内存开销。我们展示了高达2.92倍的速度比基线模型的图像，视频和3D形状表示和渲染任务。
英文摘要：
Implicit neural representations with multi-layer perceptrons (MLPs) have recently gained prominence for a wide variety of tasks such as novel view synthesis and 3D object representation and rendering. However, a significant challenge with these representations is that both training and inference with an MLP over a large number of input coordinates to learn and represent an image, video, or 3D object, require large amounts of computation and incur long processing times. In this work, we aim to accelerate inference and training of coordinate-based MLPs for implicit neural representations by proposing a new split MLP architecture, CoordX. With CoordX, the initial layers are split to learn each dimension of the input coordinates separately. The intermediate features are then fused by the last layers to generate the learned signal at the corresponding coordinate point. This significantly reduces the amount of computation required and leads to large speedups in training and inference, while achieving similar accuracy as the baseline MLP. This approach thus aims at first learning functions that are a decomposition of the original signal and then fusing them to generate the learned signal. Our proposed architecture can be generally used for many implicit neural representation tasks with no additional memory overheads. We demonstrate a speedup of up to 2.92x compared to the baseline model for image, video, and 3D shape representation and rendering tasks.
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。
