HR-NeRF: advancing realism and accuracy in highlight scene representation
NeRF and its variants excel in novel view synthesis but struggle with scenes featuring specular highlights. To address this limitation, we introduce the Highlight Recovery Network (HRNet), a new architecture that enhances NeRF's ability to capture specular scenes. HRNet incorporates Swish activ...
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| Format: | Article |
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Frontiers Media S.A.
2025-04-01
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| Series: | Frontiers in Neurorobotics |
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| Online Access: | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1558948/full |
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| author | Shufan Dai Shanqin Wang |
| author_facet | Shufan Dai Shanqin Wang |
| author_sort | Shufan Dai |
| collection | DOAJ |
| description | NeRF and its variants excel in novel view synthesis but struggle with scenes featuring specular highlights. To address this limitation, we introduce the Highlight Recovery Network (HRNet), a new architecture that enhances NeRF's ability to capture specular scenes. HRNet incorporates Swish activation functions, affine transformations, multilayer perceptrons (MLPs), and residual blocks, which collectively enable smooth non-linear transformations, adaptive feature scaling, and hierarchical feature extraction. The residual connections help mitigate the vanishing gradient problem, ensuring stable training. Despite the simplicity of HRNet's components, it achieves impressive results in recovering specular highlights. Additionally, a density voxel grid enhances model efficiency. Evaluations on four inward-facing benchmarks demonstrate that our approach outperforms NeRF and its variants, achieving a 3–5 dB PSNR improvement on each dataset while accurately capturing scene details. Furthermore, our method effectively preserves image details without requiring positional encoding, rendering a single scene in ~18 min on an NVIDIA RTX 3090 Ti GPU. |
| format | Article |
| id | doaj-art-dbf3824e991848a88cc83673feb7e8f1 |
| institution | OA Journals |
| issn | 1662-5218 |
| language | English |
| publishDate | 2025-04-01 |
| publisher | Frontiers Media S.A. |
| record_format | Article |
| series | Frontiers in Neurorobotics |
| spelling | doaj-art-dbf3824e991848a88cc83673feb7e8f12025-08-20T02:26:24ZengFrontiers Media S.A.Frontiers in Neurorobotics1662-52182025-04-011910.3389/fnbot.2025.15589481558948HR-NeRF: advancing realism and accuracy in highlight scene representationShufan DaiShanqin WangNeRF and its variants excel in novel view synthesis but struggle with scenes featuring specular highlights. To address this limitation, we introduce the Highlight Recovery Network (HRNet), a new architecture that enhances NeRF's ability to capture specular scenes. HRNet incorporates Swish activation functions, affine transformations, multilayer perceptrons (MLPs), and residual blocks, which collectively enable smooth non-linear transformations, adaptive feature scaling, and hierarchical feature extraction. The residual connections help mitigate the vanishing gradient problem, ensuring stable training. Despite the simplicity of HRNet's components, it achieves impressive results in recovering specular highlights. Additionally, a density voxel grid enhances model efficiency. Evaluations on four inward-facing benchmarks demonstrate that our approach outperforms NeRF and its variants, achieving a 3–5 dB PSNR improvement on each dataset while accurately capturing scene details. Furthermore, our method effectively preserves image details without requiring positional encoding, rendering a single scene in ~18 min on an NVIDIA RTX 3090 Ti GPU.https://www.frontiersin.org/articles/10.3389/fnbot.2025.1558948/fullscene representationview synthesisimage-based renderingvolume rendering3D deep learningspectral bias |
| spellingShingle | Shufan Dai Shanqin Wang HR-NeRF: advancing realism and accuracy in highlight scene representation Frontiers in Neurorobotics scene representation view synthesis image-based rendering volume rendering 3D deep learning spectral bias |
| title | HR-NeRF: advancing realism and accuracy in highlight scene representation |
| title_full | HR-NeRF: advancing realism and accuracy in highlight scene representation |
| title_fullStr | HR-NeRF: advancing realism and accuracy in highlight scene representation |
| title_full_unstemmed | HR-NeRF: advancing realism and accuracy in highlight scene representation |
| title_short | HR-NeRF: advancing realism and accuracy in highlight scene representation |
| title_sort | hr nerf advancing realism and accuracy in highlight scene representation |
| topic | scene representation view synthesis image-based rendering volume rendering 3D deep learning spectral bias |
| url | https://www.frontiersin.org/articles/10.3389/fnbot.2025.1558948/full |
| work_keys_str_mv | AT shufandai hrnerfadvancingrealismandaccuracyinhighlightscenerepresentation AT shanqinwang hrnerfadvancingrealismandaccuracyinhighlightscenerepresentation |