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Among the 3D target reconstruction methods, the neural radiation field is a micro-renderable process proposed on the basis of classical body rendering, which realizes the effective combination of neural field and graphics components and has shown its potential in the field of new viewpoint generation. Although the neural radiation field can generate high-quality reconstruction results, it requires rendering the scene under each viewpoint, leading to slow and time-consuming modeling. By contrast, the 3D point cloud, which is generated using the attention mechanism, presents both the spatial geometry of the object and preserves the color-texture features of the points around the object. Combining the neural radiation field with the attention point cloud avoids the rendering of missed points, resulting in improved reconstruction speed. Furthermore, the surface texture information of the object is preserved in the ray-tracing-based rendering pipeline. According to experimental results, the neural radiation field based on the attention point cloud method maintains the visual consistency of geometric contours with the original rendering method, thus achieving fast 3D target reconstruction. Quantified results based on SSIM, PSNR, and LPIPS further confirm the feasibility of this algorithmic model.
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