用量子电路提升3D高斯点云的视角依赖渲染效果
QuantumGS: Quantum Encoding Framework for Gaussian Splatting
- 将视角方向映射到量子比特球面，利用量子态几何特性编码方向信息
- 用量子电路替代传统网络，显著增强低参数场景下的颜色表达能力
- 适合关注量子机器学习与3D渲染融合的研究者和开发者
近年来，神经渲染技术特别是3D高斯点云（3DGS）实现了复杂场景的实时渲染。然而，标准3DGS依赖球谐函数，难以准确捕捉如锐利反光和透明等高频视角依赖效应。尽管混合方法如视图方向高斯点云（VDGS）采用经典多层感知机（MLPs）缓解此问题，但在低参数情况下仍受限于经典网络的表达能力。本文提出QuantumGS，一种将变分量子电路（VQC）融入高斯点云流程的新型混合框架。我们设计了一种独特编码策略，直接将视角方向映射至布洛赫球，利用量子比特的自然几何特性表示三维方向数据。通过用超网络或条件机制生成的量子电路替代经典颜色调制网络，实现更高表达力与更好泛化性能。源代码见附录，项目地址：https://github.com/gwilczynski95/QuantumGS。
原文摘要 · Abstract (English)
Recent advances in neural rendering, particularly 3D Gaussian Splatting (3DGS), have enabled real-time rendering of complex scenes. However, standard 3DGS relies on spherical harmonics, which often struggle to accurately capture high-frequency view-dependent effects such as sharp reflections and transparency. While hybrid approaches like Viewing Direction Gaussian Splatting (VDGS) mitigate this limitation using classical Multi-Layer Perceptrons (MLPs), they remain limited by the expressivity of classical networks in low-parameter regimes. In this paper, we introduce QuantumGS, a novel hybrid framework that integrates Variational Quantum Circuits (VQC) into the Gaussian Splatting pipeline. We propose a unique encoding strategy that maps the viewing direction directly onto the Bloch sphere, leveraging the natural geometry of qubits to represent 3D directional data. By replacing classical color-modulating networks with quantum circuits generated via a hypernetwork or conditioning mechanism, we achieve higher expressivity and better generalization. Source code is available in the supplementary material. Code is available at https://github.com/gwilczynski95/QuantumGS
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