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arXiv · 2308.11417

ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes

Chandan Yeshwanth·Yueh-Cheng Liu·Matthias Nießner·Angela Dai
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Abstract

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.

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Chandan Yeshwanth, Yueh-Cheng Liu, Matthias Nießner, Angela Dai. 2023-08-22. ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes. https://arxiv.org/abs/2308.11417

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