Large-Scale Indoor Visual–Geometric Multimodal Dataset and Benchmark for Novel View Synthesis

Abstract

The accurate reconstruction of indoor environments is crucial for applications in augmented reality, virtual reality, and robotics. However, existing indoor datasets are often limited in scale, lack ground truth point clouds, and provide insufficient viewpoints, which impedes the development of robust novel view synthesis (NVS) techniques. To address these limitations, we introduce a new large-scale indoor dataset that features diverse and challenging scenes, including basements and long corridors. This dataset offers panoramic image sequences for comprehensive coverage, high-resolution point clouds, meshes, and textures as ground truth, and a novel benchmark specifically designed to evaluate NVS algorithms in complex indoor environments. Our dataset and benchmark aim to advance indoor scene reconstruction and facilitate the creation of more effective NVS solutions for real-world applications.