ScanNet++: 一个高保真的3D室内场景数据集
ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
AI总结：
本研究提出大规模高保真3D室内场景数据集ScanNet++，融合多设备采集的高精度与消费级数据，标注开放词汇语义及模糊场景，为新视角合成和3D语义理解提供新基准，目前包含460个场景及海量采集数据。
摘要：
我们提出了ScanNet++，这是一个大规模数据集，将室内场景的高质量与消费级几何结构和色彩采集相结合。每个场景均由高端激光扫描仪以亚毫米分辨率采集，同时搭配已配准的数码单反相机3300万像素图像，以及iPhone采集的RGB-D数据流。场景重建结果进一步标注了开放词汇语义，还对标签模糊场景进行了明确标注，以支持全面的语义理解。除了新增的3D语义场景理解基准（全面涵盖多样且模糊的语义标注场景）外，ScanNet++还为新视角合成提供了全新的真实世界基准，既支持基于高质量RGB采集的任务，也同样重要地支持基于消费级图像的任务。目前，ScanNet++包含460个场景、28万张采集的数码单反图像，以及超过370万帧iPhone RGBD帧。
英文摘要：
We present ScanNet++, a large-scale dataset that couples together capture of high-quality and commodity-level geometry and color of indoor scenes. Each scene is captured with a high-end laser scanner at sub-millimeter resolution, along with registered 33-megapixel images from a DSLR camera, and RGB-D streams from an iPhone. Scene reconstructions are further annotated with an open vocabulary of semantics, with label-ambiguous scenarios explicitly annotated for comprehensive semantic understanding. ScanNet++ enables a new real-world benchmark for novel view synthesis, both from high-quality RGB capture, and importantly also from commodity-level images, in addition to a new benchmark for 3D semantic scene understanding that comprehensively encapsulates diverse and ambiguous semantic labeling scenarios. Currently, ScanNet++ contains 460 scenes, 280,000 captured DSLR images, and over 3.7M iPhone RGBD frames.
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。
