用外引导压缩Voxel辐射场的压缩
概括
引导压缩Voxel辐射场 (SVRF) 在3D重建中减少了70%以上的内存使用. 这种新的方法将voxel模型优化为外结构,通过自我监督学习保持准确性.
科学领域:
- 计算机视觉 计算机视觉
- 3D 图形 3D 图形
- 机器学习 机器学习
背景情况:
- 在3D重建中的基于voxel的大型模型遭受了高内存消耗和冗余数据的困扰.
- 现有的方法往往难以平衡存储效率与染精度.
研究的目的:
- 引入一种新的压缩方法,即对Voxel辐射场 (SVRF) 的 Shell 引导压缩,用于优化基于voxel的模型.
- 在3D重建中显著降低存储成本,同时保持染质量.
主要方法:
- 实施了类似贝的约束,以优先考虑与表面相邻的voxels,并消除冗余的voxels.
- 使用自适应值和动态修剪策略,以精确和适合场景的删除voxel.
- 员工自主监督学习以预测的深度为指导,不需要额外的标签.
主要成果:
- 实现了70%以上的voxel网格压缩.
- 保持了与原始模型相比较的染质量.
- 证明了与现有的基于voxel-grid的方法的无集成.
结论:
- 在大规模的voxel模型中,SVRF为存储和存储挑战提供了有效的解决方案.
- 该方法是高效的,准确的,广泛适用于各种3D重建任务.
- 拟议的方法促进了复杂的3D模型的实际部署.
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