InteriorNet is a RGB-D for large scale interior scene understanding and mapping. The dataset contains 20M images created by pipeline:
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Estimating camera motion in deformable scenes poses a complex and open research challenge. Most existing non-rigid structure from motion techniques assume to observe also static scene parts besides deforming scene parts in order to establish an anchoring reference. However, this assumption does not hold true in certain relevant application cases such as endoscopies. To tackle this issue with a common benchmark, we introduce the Drunkard’s Dataset, a challenging collection of synthetic data targeting visual navigation and reconstruction in deformable environments. This dataset is the first large set of exploratory camera trajectories with ground truth inside 3D scenes where every surface exhibits non-rigid deformations over time. Simulations in realistic 3D buildings lets us obtain a vast amount of data and ground truth labels, including camera poses, RGB images and depth, optical flow and normal maps at high resolution and quality.
1 PAPER • 1 BENCHMARK
HPointLoc is a dataset designed for exploring capabilities of visual place recognition in indoor environment and loop detection in simultaneous localization and mapping. It is based on the popular Habitat simulator from 49 photorealistic indoor scenes from the Matterport3D dataset and contains 76,000 frames.
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PolyU-BPCoMa: A Dataset and Benchmark Towards Mobile Colorized Mapping Using a Backpack Multisensorial System