三维场景图:统一语义、三维空间和相机的结构

Iro Armeni, Zhi-Yang He, JunYoung Gwak, A. Zamir, Martin Fischer, J. Malik, S. Savarese
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引用次数: 198

摘要

对场景的全面语义理解对于许多应用程序都很重要,但是在什么空间中应该有不同的语义信息(例如,对象、场景类别、材料类型、3D形状等),它的结构应该是什么?渴望有一个统一的结构,承载不同类型的语义,我们遵循场景图范式在3D,生成一个3D场景图。给定一个3D网格和注册的全景图像,我们构建一个跨越整个建筑物的图,包括对象(例如,类别,材料,形状和其他属性),房间(例如,功能,照明类型等)和相机(例如,位置等)的语义,以及这些实体之间的关系。然而,如果手工完成,这个过程是非常繁重的劳动。为了缓解这种情况,我们设计了一个半自动框架,该框架采用现有的检测方法,并使用两个主要约束来增强它们:1 .在全景图上采样查询图像的框架,以最大化2D检测器的性能;跨2D检测的多视图一致性强制执行,起源于不同的相机位置。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
3D Scene Graph: A Structure for Unified Semantics, 3D Space, and Camera
A comprehensive semantic understanding of a scene is important for many applications - but in what space should diverse semantic information (e.g., objects, scene categories, material types, 3D shapes, etc.) be grounded and what should be its structure? Aspiring to have one unified structure that hosts diverse types of semantics, we follow the Scene Graph paradigm in 3D, generating a 3D Scene Graph. Given a 3D mesh and registered panoramic images, we construct a graph that spans the entire building and includes semantics on objects (e.g., class, material, shape and other attributes), rooms (e.g., function, illumination type, etc.) and cameras (e.g., location, etc.), as well as the relationships among these entities. However, this process is prohibitively labor heavy if done manually. To alleviate this we devise a semi-automatic framework that employs existing detection methods and enhances them using two main constraints: I. framing of query images sampled on panoramas to maximize the performance of 2D detectors, and II. multi-view consistency enforcement across 2D detections that originate in different camera locations.
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