OpenHype：用于层级开放词汇辐射场的双曲嵌入
OpenHype: Hyperbolic Embeddings for Hierarchical Open-Vocabulary Radiance Fields
AI总结：
OpenHype提出用连续双曲潜在空间表示3D场景层级，通过测地线路径实现多尺度关系编码与平滑遍历，在标准基准上超越现有方法，提升效率与适应性。
摘要：
对3D物体和3D场景固有的层级结构进行建模是非常可取的，因为它能使自主智能体更全面地理解环境。然而，使用隐式表示（如神经辐射场）来实现这一点仍然是一个尚未探索的挑战。现有的显式建模层级结构的方法常常面临显著的限制：它们要么需要多次渲染传递来捕获不同粒度级别的嵌入，从而显著增加推理时间，要么依赖于预定义的、封闭集的离散层级，这些层级难以泛化到智能体在现实世界中遇到的各种细微结构。为了解决这些挑战，我们提出了OpenHype，一种使用连续双曲潜在空间来表示场景层级的新方法。通过利用双曲几何的性质，OpenHype自然地编码了多尺度关系，并能够通过潜在空间中的测地线路径平滑地遍历层级。我们的方法在标准基准上优于最先进的方法，展示了在3D场景理解中卓越的效率和适应性。
英文摘要：
Modeling the inherent hierarchical structure of 3D objects and 3D scenes is highly desirable, as it enables a more holistic understanding of environments for autonomous agents. Accomplishing this with implicit representations, such as Neural Radiance Fields, remains an unexplored challenge. Existing methods that explicitly model hierarchical structures often face significant limitations: they either require multiple rendering passes to capture embeddings at different levels of granularity, significantly increasing inference time, or rely on predefined, closed-set discrete hierarchies that generalize poorly to the diverse and nuanced structures encountered by agents in the real world. To address these challenges, we propose OpenHype, a novel approach that represents scene hierarchies using a continuous hyperbolic latent space. By leveraging the properties of hyperbolic geometry, OpenHype naturally encodes multi-scale relationships and enables smooth traversal of hierarchies through geodesic paths in latent space. Our method outperforms state-of-the-art approaches on standard benchmarks, demonstrating superior efficiency and adaptability in 3D scene understanding.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
