PanoContext: A Whole-Room 3D Context Model for Panoramic Scene Understanding. The field-of-view of standard cameras is very small, which is one of the main reasons that contextual information is not as useful as it should be for object detection. To overcome this limitation, we advocate the use of 360° full-view panoramas in scene understanding, and propose a whole-room context model in 3D. For an input panorama, our method outputs 3D bounding boxes of the room and all major objects inside, together with their semantic categories. Our method generates 3D hypotheses based on contextual constraints and ranks the hypotheses holistically, combining both bottom-up and top-down context information. To train our model, we construct an annotated panorama dataset and reconstruct the 3D model from single-view using manual annotation. Experiments show that solely based on 3D context without any image region category classifier, we can achieve a comparable performance with the state-of-the-art object detector. This demonstrates that when the FOV is large, context is as powerful as object appearance. All data and source code are available online.

References in zbMATH (referenced in 3 articles )

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  1. Qian Xie, Yu-Kun Lai, Jing Wu, Zhoutao Wang, Yiming Zhang, Kai Xu, Jun Wang: MLCVNet: Multi-Level Context VoteNet for 3D Object Detection (2020) arXiv
  2. Liu, Niantao; Lin, Bingxian; Yuan, Linwang; Lv, Guonian; Yu, Zhaoyuan; Zhou, Liangchen: An interactive indoor 3D reconstruction method based on conformal geometry algebra (2018)
  3. Zhang, Yinda; Song, Shuran; Tan, Ping; Xiao, Jianxiong: Panocontext: A whole-room 3D context model for panoramic scene understanding (2014) ioport