Tri-Planar NeRFs enable the application of powerful 2D vision models for 3D tasks, by representing 3D objects using 2D planar structures. This has made them the prevailing choice to model large collections of 3D objects. However, training Tri-Planes to model such large collections is computationally intensive and remains largely inefficient. This is because the current approaches independently train one Tri-Plane per object, hence overlooking structural similarities in large classes of objects. In response to this issue, we introduce Fused-Planes, a novel object representation that improves the resource efficiency of Tri-Planes when reconstructing object classes, all while retaining the same planar structure. Our approach explicitly captures structural similarities across objects through a latent space and a set of globally shared base planes. Each individual Fused-Planes is then represented as a decomposition over these base planes, augmented with object-specific features. Fused-Planes showcase state-of-the-art efficiency among planar representations, demonstrating $7.2 \times$ faster training and $3.2 \times$ lower memory footprint than Tri-Planes while maintaining rendering quality. An ultra-lightweight variant further cuts per-object memory usage by $1875 \times$ with minimal quality loss. Our project page can be found at https://fused-planes.github.io .

翻译：三平面神经辐射场通过利用二维平面结构表示三维物体，使得强大的二维视觉模型能够应用于三维任务。这使其成为建模大规模三维物体集合的主流选择。然而，为建模此类大规模集合而训练三平面模型计算成本高昂，且效率普遍低下。这是因为当前方法需为每个物体独立训练一个三平面模型，从而忽视了同类物体间的结构相似性。针对此问题，我们提出融合平面——一种新颖的物体表征方法，在重建物体类别时显著提升三平面模型的资源效率，同时完全保留其平面结构。我们的方法通过潜在空间和一组全局共享的基础平面，显式捕获跨物体的结构相似性。每个独立的融合平面表征均可分解为这些基础平面的加权组合，并辅以物体特异性特征增强。融合平面在平面表征中展现出最先进的效率：在保持渲染质量的同时，训练速度比三平面模型提升$7.2$倍，内存占用降低$3.2$倍。其超轻量变体进一步将单物体内存使用量削减$1875$倍，且质量损失极小。项目页面详见 https://fused-planes.github.io 。