Towards High-Fidelity Gaussian Splatting with Queried-Convolution Neural Networks
Fuente:
arXiv
Saved in:
| Main Authors: | , , , , , , |
|---|---|
| Format: | Preprint |
| Published: |
2025
|
| Subjects: | |
| Online Access: | |
| Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
| _version_ | 1866918387595083776 |
|---|---|
| author | Kumar, Abhinav Aumentado-Armstrong, Tristan Valkov, Lazar Sharma, Gopal Levinshtein, Alex Grzeszczuk, Radek Kumar, Suren |
| author_facet | Kumar, Abhinav Aumentado-Armstrong, Tristan Valkov, Lazar Sharma, Gopal Levinshtein, Alex Grzeszczuk, Radek Kumar, Suren |
| contents | 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. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2512_12898 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Towards High-Fidelity Gaussian Splatting with Queried-Convolution Neural Networks Kumar, Abhinav Aumentado-Armstrong, Tristan Valkov, Lazar Sharma, Gopal Levinshtein, Alex Grzeszczuk, Radek Kumar, Suren Computer Vision and Pattern Recognition Graphics Machine Learning 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. |
| title | Towards High-Fidelity Gaussian Splatting with Queried-Convolution Neural Networks |
| topic | Computer Vision and Pattern Recognition Graphics Machine Learning |
| url | https://arxiv.org/abs/2512.12898 |