Implicit Neural Point Cloud (INPC) is a recent hybrid representation that combines the expressiveness of neural fields with the efficiency of point-based rendering, achieving state-of-the-art image quality in novel view synthesis. However, as with other high-quality approaches that query neural networks during rendering, the practical usability of INPC is limited by comparatively slow rendering. In this work, we present a collection of optimizations that significantly improve both the training and inference performance of INPC without sacrificing visual fidelity. The most significant modifications are an improved rasterizer implementation, more effective sampling techniques, and the incorporation of pre-training for the convolutional neural network used for hole-filling. Furthermore, we demonstrate that points can be modeled as small Gaussians during inference to further improve quality in extrapolated, e.g., close-up views of the scene. We design our implementations to be broadly applicable beyond INPC and systematically evaluate each modification in a series of experiments. Our optimized INPC pipeline achieves up to 25% faster training, 2x faster rendering, and 20% reduced VRAM usage paired with slight image quality improvements.

翻译：隐式神经点云（INPC）是一种新兴的混合表示方法，它融合了神经场（neural fields）的表达能力与基于点的渲染效率，在新视角合成任务中实现了最先进的图像质量。然而，与其它在渲染过程中查询神经网络的高质量方法类似，INPC的实际应用受限于相对缓慢的渲染速度。本文提出一系列优化技术，在不牺牲视觉保真度的前提下，显著提升了INPC的训练与推理性能。其中最重要的改进包括：优化的光栅化器实现、更有效的采样技术，以及为用于孔洞填充的卷积神经网络引入预训练。此外，我们证明了在推理过程中将点建模为小型高斯分布可以进一步提升外推视图（例如场景特写视图）的质量。我们的实现设计具有超越INPC的广泛适用性，并通过一系列实验系统评估了每项改进。优化后的INPC流程实现了高达25%的训练加速、2倍的渲染加速以及20%的显存使用降低，同时伴随小幅图像质量提升。