[论文] Honeycomb-Surfels: Compact Hexagonal Primitives for Efficient Scene Re...

论文概要

研究领域: CV / 3D重建 作者: Prakhar Kaushik, Soumava Paul, Alan Yuille 发布时间: 2026-10-27 arXiv: 2610.37668

中文摘要

神经辐射场和Splatting技术虽推动了三维重建和新视角合成的快速发展，但其巨大的内存和计算开销限制了在资源受限场景中的应用。本文提出Hexels——一种新型平面六边形表面基元，每个基元内置一阶空间颜色场，用于高效的3D场景和2D图像表示。Hexels利用局部平面区域外观平滑变化的特点，用更少、更大的基元完成表示。六边形支撑域在面积和边界效率上优于四边形。在34个场景上，Hexels在同等内存条件下比2DGS提升PSNR 4.53-7.38 dB，且训练显存更低。逐基元对比显示，Hexels在更低训练显存下比2DGS最高提升4.74 dB，FPS快2.31倍。在3D表面重建任务中，Hexels在15个DTU扫描上将误差降低28.9%；在6个Tanks and Temples表面场景上取得最高平均F1（34.38%，对比2DGS的31.72%）。Hexels推动了质量-效率权衡的帕累托前沿。

原文摘要

Novel view synthesis and 3D reconstruction have advanced rapidly with radiance fields and splatting, yet their memory and computational demands limit their use in resource-constrained settings. We introduce Hexels: novel planar hexagonal surface elements with a first-order spatial color field for efficient 3D scene and 2D image representation. Hexels exploit locally planar regions with smoothly varying appearance using fewer, larger primitives. Their hexagonal support provides an area- and boundary-efficient domain, while the local color field captures appearance variation within each primitive. Across 34 scenes, Hexels improve PSNR over memory-matched 2DGS by 4.53-7.38 dB across datasets while requiring lower training VRAM. We further ablate Hexels by showing that per primitive, Hexels outperform 2DGS by upto 4.74 dB while using lower training VRAM and having 2.31 times faster FPS. Even in unconstrained settings, Hexels offers complementary quality-memory trade-offs. For efficient 3D surface reconstruction, Hexels reduce error by 28.9% compared with memory-matched 2DGS across 15 DTU scans. On 6 Tanks and Temples surface scenes, Hexels achieves the highest mean F1 among compared methods (34.38%, versus 31.72% for 2DGS). We further also show that Hexels, as a general primitive, improves 2D image representation over Image-GS. Hexels push the Pareto frontier on the quality-efficiency trade-off of surface-aligned primitives.

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*自动采集于 2026-10-29*

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