SUNCG

doi:doi:10.57702/zpty8syx

SUNCG

The SUNCG dataset is a large dataset of 3D indoor scenes with missing objects. The dataset is used to evaluate the performance of scene augmentation and context-based object recognition methods.

BibTex:

@dataset{Siyuan_Qi_and_Yixin_Zhu_and_Siyuan_Huang_and_Chenfanfu_Jiang_and_Song-Chun_Zhu_2024,
    abstract = {The SUNCG dataset is a large dataset of 3D indoor scenes with missing objects. The dataset is used to evaluate the performance of scene augmentation and context-based object recognition methods.},
    author = {Siyuan Qi and Yixin Zhu and Siyuan Huang and Chenfanfu Jiang and Song-Chun Zhu},
    doi = {10.57702/zpty8syx},
    institution = {No Organization},
    keyword = {'3D Indoor Scene', '3D Navigation', '3D Scene Generation', '3D Scenes', '3D reconstruction', '3D scene analysis', '3D scene reconstruction', '3D segmentation', 'Indoor Environment', 'Object Recognition', 'Scene Augmentation', 'Synthetic Environment', 'Synthetic Scenes', 'depth image', 'geometry', 'illumination', 'indoor scene', 'indoor scene understanding', 'indoor scenes', 'inverse rendering', 'low geometric complexity', 'realistic layouts', 'reﬂectance', 'room and furniture', 'room environments', 'semantic scene completion', 'semantic segmentation', 'simplistic house', 'single image', 'sparse views', 'texture mapped surfaces', 'volumetric reconstruction'},
    month = {dec},
    publisher = {TIB},
    title = {SUNCG},
    url = {https://service.tib.eu/ldmservice/dataset/suncg},
    year = {2024}
}