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

TL;DR

ScanNet++ is a comprehensive high-fidelity 3D indoor scene dataset combining high-resolution laser scans, DSLR images, and RGB-D streams, designed to advance benchmarks in novel view synthesis and semantic understanding.

Contribution

It introduces a large-scale, multimodal dataset with detailed annotations and ambiguous semantic labels, enabling new research in 3D scene reconstruction and understanding.

Findings

Enables high-quality novel view synthesis from diverse image sources

Provides extensive semantic annotations for complex indoor scenes

Supports benchmarking for 3D semantic scene understanding

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.