A Comparative Evaluation of Geometric Accuracy in NeRF and Gaussian Splatting
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arXiv
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| Main Authors: | , , , , , |
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| Format: | Preprint |
| Published: |
2026
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| _version_ | 1866910148639850496 |
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| author | Zielinski, Mikolaj Vykysaly, Eryk Biesiada, Bartlomiej Baturo, Jan Capala, Mateusz Belter, Dominik |
| author_facet | Zielinski, Mikolaj Vykysaly, Eryk Biesiada, Bartlomiej Baturo, Jan Capala, Mateusz Belter, Dominik |
| contents | Recent advances in neural rendering have introduced numerous 3D scene representations. Although standard computer vision metrics evaluate the visual quality of generated images, they often overlook the fidelity of surface geometry. This limitation is particularly critical in robotics, where accurate geometry is essential for tasks such as grasping and object manipulation. In this paper, we present an evaluation pipeline for neural rendering methods that focuses on geometric accuracy, along with a benchmark comprising 19 diverse scenes. Our approach enables a systematic assessment of reconstruction methods in terms of surface and shape fidelity, complementing traditional visual metrics. |
| format | Preprint |
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arxiv_https___arxiv_org_abs_2604_18205 |
| institution | arXiv |
| publishDate | 2026 |
| record_format | arxiv |
| spellingShingle | A Comparative Evaluation of Geometric Accuracy in NeRF and Gaussian Splatting Zielinski, Mikolaj Vykysaly, Eryk Biesiada, Bartlomiej Baturo, Jan Capala, Mateusz Belter, Dominik Computer Vision and Pattern Recognition Robotics Recent advances in neural rendering have introduced numerous 3D scene representations. Although standard computer vision metrics evaluate the visual quality of generated images, they often overlook the fidelity of surface geometry. This limitation is particularly critical in robotics, where accurate geometry is essential for tasks such as grasping and object manipulation. In this paper, we present an evaluation pipeline for neural rendering methods that focuses on geometric accuracy, along with a benchmark comprising 19 diverse scenes. Our approach enables a systematic assessment of reconstruction methods in terms of surface and shape fidelity, complementing traditional visual metrics. |
| title | A Comparative Evaluation of Geometric Accuracy in NeRF and Gaussian Splatting |
| topic | Computer Vision and Pattern Recognition Robotics |
| url | https://arxiv.org/abs/2604.18205 |