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Existing research on multi-view stereo scheme utilizes depth-estimation algorithms to achieve stereo representation by establishing a mapping relationship between the physical and digital worlds. Supervised learning-based neural networks have achieved accurate and high-fidelity 3D reconstruction results through training. However, in-the-wild visual reconstruction remains challenging due to the lack of rendered depth priors and wide-baseline characteristics of images. A novel system was proposed to obtain optimized depth for naturally collected multi-view images without prior information by applying an unsupervised learning network and semantically optimized Neural Radiation Field (NeRF) rendering. First, preliminary depth information for wild multi-view images were produced without ground truth based on unsupervised deep learning. Subsequently, in a separate NeRF module, a diffusion model was used to construct a surface semantic rendering loss, enabling a fine-grained volumetric representation. Experimental results on the benchmark dataset validated the performance of the proposed system by improving an average of 24.6% of the overall metrics, compared with other state-of-the-art schemes. A novel wild wide-baseline dataset was also applied to verify the generalization performance, and the proposed system reduced the reconstruction error by up to 40.8% compared with all methods.
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| 科 Family | 属数 Number of genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) | 属 Genus | 种数 Number of species | 占总种数比例 Percentage of total species (%) |
|---|---|---|---|---|---|---|
| 鹅膏菌科Amanitaceae | 2 | 11 | 5.26 | 鹅膏菌属 Amanita | 10 | 4.78 |
| 小菇科 Mycenaceae | 2 | 12 | 5.74 | 丝盖伞属 Inocybe | 5 | 2.39 |
| 多孔菌科 Polyporaceae | 8 | 14 | 6.70 | 蜡蘑属 Laccaria | 5 | 2.39 |
| 红菇科 Russulaceae | 3 | 23 | 11.00 | 小皮伞属 Marasmius | 6 | 2.87 |
| 小菇属 Mycena | 11 | 5.26 | ||||
| 光柄菇属 Pluteus | 5 | 2.39 | ||||
| 红菇属 Russula | 17 | 8.13 | ||||
| 栓菌属 Trametes | 5 | 2.39 |
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