InteriorNet: Mega-scale Multi-sensor Photo-realistic Indoor Scenes Dataset

Datasets have gained an enormous amount of popularity in the computer vision community, from training and evaluation of Deep Learning-based methods to benchmarking Simultaneous Localization and Mapping (SLAM). Without a doubt, synthetic imagery bears a vast potential due to scalability in terms of amounts of data obtainable without tedious manual ground truth annotations or measurements. Here, we present a dataset with the aim of providing a higher degree of photo-realism, larger scale, more variability as well as serving a wider range of purposes compared to existing datasets. Our dataset leverages the availability of millions of professional interior designs and millions of production-level furniture and object assets -- all coming with fine geometric details and high-resolution texture. We render high-resolution and high frame-rate video sequences following realistic trajectories while supporting various camera types as well as providing inertial measurements. Together with the release of the dataset, we will make executable program of our interactive simulator software as well as our renderer available at https://interiornetdataset.github.io. To showcase the usability and uniqueness of our dataset, we show benchmarking results of both sparse and dense SLAM algorithms

MageAdd: Real-Time Interaction Simulation for Scene Synthesis

While recent researches on computational 3D scene synthesis have achieved impressive results, automatically synthesized scenes do not guarantee satisfaction of end users. On the other hand, manual scene modelling can always ensure high quality, but requires a cumbersome trial-and-error process. In this paper, we bridge the above gap by presenting a data-driven 3D scene synthesis framework that can intelligently infer objects to the scene by incorporating and simulating user preferences with minimum input. While the cursor is moved and clicked in the scene, our framework automatically selects and transforms suitable objects into scenes in real time. This is based on priors learnt from the dataset for placing different types of objects, and updated according to the current scene context. Through extensive experiments we demonstrate that our framework outperforms the state-of-the-art on result aesthetics, and enables effective and efficient user interactions