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We present ScanNet++, a large-scale dataset that couples together capture ofhigh-quality and commodity-level geometry and color of indoor scenes. Eachscene 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 streamsfrom an iPhone. Scene reconstructions are further annotated with an openvocabulary of semantics, with label-ambiguous scenarios explicitly annotatedfor comprehensive semantic understanding. ScanNet++ enables a new real-worldbenchmark for novel view synthesis, both from high-quality RGB capture, andimportantly also from commodity-level images, in addition to a new benchmarkfor 3D semantic scene understanding that comprehensively encapsulates diverseand ambiguous semantic labeling scenarios. Currently, ScanNet++ contains 460scenes, 280,000 captured DSLR images, and over 3.7M iPhone RGBD frames.

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