TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes
Fuente:
arXiv
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| Autores principales: | , , |
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| Formato: | Preprint |
| Publicado: |
2025
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| _version_ | 1866916727798890496 |
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| author | Aggarwal, Alakh Wang, Ningna Guo, Xiaohu |
| author_facet | Aggarwal, Alakh Wang, Ningna Guo, Xiaohu |
| contents | Reconstructing 3D models of dynamic, real-world objects with high-fidelity textures from monocular frame sequences has been a challenging problem in recent years. This difficulty stems from factors such as shadows, indirect illumination, and inaccurate object-pose estimations due to occluding hand-object interactions. To address these challenges, we propose a novel approach that predicts the hand's impact on environmental visibility and indirect illumination on the object's surface albedo. Our method first learns the geometry and low-fidelity texture of the object, hand, and background through composite rendering of radiance fields. Simultaneously, we optimize the hand and object poses to achieve accurate object-pose estimations. We then refine physics-based rendering parameters - including roughness, specularity, albedo, hand visibility, skin color reflections, and environmental illumination - to produce precise albedo, and accurate hand illumination and shadow regions. Our approach surpasses state-of-the-art methods in texture reconstruction and, to the best of our knowledge, is the first to account for hand-object interactions in object texture reconstruction. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_03525 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes Aggarwal, Alakh Wang, Ningna Guo, Xiaohu Computer Vision and Pattern Recognition Reconstructing 3D models of dynamic, real-world objects with high-fidelity textures from monocular frame sequences has been a challenging problem in recent years. This difficulty stems from factors such as shadows, indirect illumination, and inaccurate object-pose estimations due to occluding hand-object interactions. To address these challenges, we propose a novel approach that predicts the hand's impact on environmental visibility and indirect illumination on the object's surface albedo. Our method first learns the geometry and low-fidelity texture of the object, hand, and background through composite rendering of radiance fields. Simultaneously, we optimize the hand and object poses to achieve accurate object-pose estimations. We then refine physics-based rendering parameters - including roughness, specularity, albedo, hand visibility, skin color reflections, and environmental illumination - to produce precise albedo, and accurate hand illumination and shadow regions. Our approach surpasses state-of-the-art methods in texture reconstruction and, to the best of our knowledge, is the first to account for hand-object interactions in object texture reconstruction. |
| title | TexHOI: Reconstructing Textures of 3D Unknown Objects in Monocular Hand-Object Interaction Scenes |
| topic | Computer Vision and Pattern Recognition |
| url | https://arxiv.org/abs/2501.03525 |