QuantumGS: Quantum Encoding Framework for Gaussian Splatting
Grzegorz Wilczy\'nski, Rafa{\l} Tobiasz, Pawe{\l} Gora, Marcin Mazur, Przemys{\l}aw Spurek

TL;DR
QuantumGS introduces a hybrid quantum-classical framework for neural rendering that enhances the modeling of view-dependent effects in 3D Gaussian Splatting by leveraging quantum encoding of viewing directions.
Contribution
It proposes a novel quantum encoding strategy and integrates variational quantum circuits into the Gaussian Splatting pipeline, improving expressivity over classical methods.
Findings
Enhanced rendering of high-frequency view-dependent effects
Higher expressivity with quantum circuits compared to classical networks
Open-source implementation available
Abstract
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…
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Taxonomy
TopicsQuantum many-body systems · Quantum Computing Algorithms and Architecture · Neural Networks and Reservoir Computing
