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arXiv · 2512.12898

Towards High-Fidelity Gaussian Splatting with Queried-Convolution Neural Networks

Abhinav Kumar·Tristan Aumentado-Armstrong·Lazar Valkov·Gopal Sharma·Alex Levinshtein·Radek Grzeszczuk·Suren Kumar
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Abstract

Gaussian Splatting has revolutionized the field of Novel View Synthesis (NVS) with faster training and real-time rendering. However, its reconstruction fidelity still trails behind the powerful radiance models such as Zip-NeRF. Motivated by our theoretical result that both queries (such as coordinates) and neighborhood are important to learn high-fidelity signals, this paper proposes Queried-Convolutions (Qonvolutions), a simple yet powerful modification using the neighborhood properties of convolution. Qonvolutions convolve a low-fidelity signal with queries to output residual and achieve high-fidelity reconstruction. We empirically demonstrate that combining Gaussian splatting with Qonvolution neural networks (QNNs) results in state-of-the-art NVS on real-world scenes, even outperforming Zip-NeRF on image fidelity. QNNs also enhance performance of 1D regression, 2D regression and 2D super-resolution tasks.

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Abhinav Kumar, Tristan Aumentado-Armstrong, Lazar Valkov, Gopal Sharma, Alex Levinshtein, Radek Grzeszczuk, Suren Kumar. 2025-12-15. Towards High-Fidelity Gaussian Splatting with Queried-Convolution Neural Networks. https://arxiv.org/abs/2512.12898

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