用曲率约束提升3D激光序列的神经距离场建模精度
CCNDF: Curvature Constrained Neural Distance Fields from 3D LiDAR Sequences
- 利用符号距离场的二阶导数约束优化学习过程
- 在大规模室外场景中实现更准确的几何重建
- 适合需要高精度3D地图与定位的应用场景
神经距离场（NDF）已成为解决3D计算机视觉与图形学下游任务的重要工具。尽管已有研究尝试从多种传感器数据中学习NDF，但在大规模室外场景中训练时缺乏真实标签，导致监督困难。以往方法依赖预设的符号距离进行引导，但往往忽视表面几何的精确建模，且仅适用于小规模场景。为此，本文提出一种新方法，利用符号距离场的二阶导数信息来增强神经场学习。该方法能更准确地估计符号距离，从而获得对底层几何结构的更全面理解。我们在映射与定位任务上对比了主流方法，结果表明所提方法性能更优，展现出在计算机视觉与图形应用中提升神经距离场能力的巨大潜力。
原文摘要 · Abstract (English)
Neural distance fields (NDF) have emerged as a powerful tool for addressing challenges in 3D computer vision and graphics downstream problems. While significant progress has been made to learn NDF from various kind of sensor data, a crucial aspect that demands attention is the supervision of neural fields during training as the ground-truth NDFs are not available for large-scale outdoor scenes. Previous works have utilized various forms of expected signed distance to guide model learning. Yet, these approaches often need to pay more attention to critical considerations of surface geometry and are limited to small-scale implementations. To this end, we propose a novel methodology leveraging second-order derivatives of the signed distance field for improved neural field learning. Our approach addresses limitations by accurately estimating signed distance, offering a more comprehensive understanding of underlying geometry. To assess the efficacy of our methodology, we conducted comparative evaluations against prevalent methods for mapping and localization tasks, which are primary application areas of NDF. Our results demonstrate the superiority of the proposed approach, highlighting its potential for advancing the capabilities of neural distance fields in computer vision and graphics applications.
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