ScanNet++: A High-Fidelity Dataset of 3D Indoor Scenes
Chandan Yeshwanth Yueh-Cheng Liu Matthias Nießner Angela Dai
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.