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Scene complexity dynamic perception for 3D reconstruction
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AI
DOI：10.1016/j.displa.2026.103391.png)
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Abstract
En 中文
With the wide application of 3D reconstruction technology in many fields, its efficient realization has become a research focus. The traditional 3D reconstruction method often adopts a relatively fixed mode when facing indoor single-scene and outdoor multi-scene, which is challenging to adjust flexibly according to the scene's complexity. Therefore, this paper proposes a 3D reconstruction method based on the dynamic perception of scene complexity. To begin with, the scene complexity system is constructed. Next, based on the binary mask technology of transparency and volume, the points in the scene with minimal contribution are screened out. Subsequently, we combine the scene complexity with the octree structure to realize the spatial dynamic streamlining, which ensures the rendering quality and significantly improves the system efficiency at the same time. We conduct comparative experiments on Mip-NeRF 360, Tanks&Temples, and Deep Blending datasets to demonstrate that our method outperforms existing evaluation metrics and visual quality, thus validating its effectiveness.
Keywords:
Scene complexity
3D reconstruction
Dynamic perception
Binary mask
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2.1K
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