清华大学与哈佛大学携手共进，推出了一项名为 LangSplat 的新型 AI 系统。该系统在三维空间内对开放式词汇进行高效、准确地搜索，为 3D 场景描述带来了革命性的创新。作为第一个基于 3DGS（三维空间语义图）的 3D 语言场方法，LangSplat 特别引入了 SAM（场景注意力模型）和 CLIP（编码器-解码器架构），在开放词汇 3D 对象定位和语义分割任务上取得了显著的成果，不仅优于当前最先进的方法，还比 LERF（一种 3D 场景表示方法）快了 199 倍。
这项突破性的技术为 3D 场景的描述和理解提供了新的可能性，使得 AI 系统能够更加准确地理解和模拟现实世界。据悉，LangSplat 的研发团队由清华和哈佛的专家组成，他们在人工智能、计算机视觉等领域拥有丰富的经验。此次合作，标志着中西方在 AI 领域交流与合作的进一步加强。
LangSplat 的问世，不仅为学术研究提供了新的思路和方法，也有望在工业应用中发挥重要作用。例如，在虚拟现实、增强现实、机器人导航等领域，LangSplat 的精确描述能力将为相关技术的发展提供有力支持。
英文标题：Tsinghua & Harvard Unveil LangSplat: A New Era for 3D Scene Description
关键词：AI system, 3DGS, SAM and CLIP
英文内容：
In a groundbreaking collaboration, Tsinghua University and Harvard University have jointly developed an innovative AI system called LangSplat. This system enables efficient and accurate search of open-ended vocabulary within three-dimensional spaces, heralding a revolutionary innovation in 3D scene description. As the first method based on 3DGS (Three-Dimensional Semantic Graph), LangSplat particularly introduces Scene Attention Model (SAM) and Encoder-Decoder architecture (CLIP), achieving significant improvements over the current state-of-the-art in open-vocabulary 3D object localization and semantic segmentation tasks, while being 199 times faster than LERF, a method for 3D scene representation.
This groundbreaking technology opens up new possibilities for the description and understanding of 3D scenes, enabling AI systems to accurately understand and simulate the real world. It is reported that the research team behind LangSplat consists of experts from Tsinghua and Harvard, with rich experience in fields such as artificial intelligence and computer vision. This collaboration marks a further strengthening of Sino-Western exchanges and cooperation in the field of AI.
The introduction of LangSplat not only provides new insights and methods for academic research but also holds great potential for industrial applications. For instance, in areas such as virtual reality, augmented reality, and robot navigation, LangSplat’s precise description capabilities are expected to offer strong support for the development of related technologies.
【来源】https://www.ithome.com/0/742/887.htm
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