PanoContext-Former: Panoramic Total Scene Understanding with a Transformer

• URL: http://arxiv.org/abs/2305.12497v2
• Date: Mon, 5 Jun 2023 04:43:41 GMT
• Title: PanoContext-Former: Panoramic Total Scene Understanding with a Transformer
• Authors: Yuan Dong, Chuan Fang, Liefeng Bo, Zilong Dong, Ping Tan
• Abstract summary: Panoramic image enables deeper understanding and more holistic perception of $360circ$ surrounding environment. In this paper, we propose a novel method using depth prior for holistic indoor scene understanding. In addition, we introduce a real-world dataset for scene understanding, including photo-realistic panoramas, high-fidelity depth images, accurately annotated room layouts, and oriented object bounding boxes and shapes.
• Score: 37.51637352106841
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Panoramic image enables deeper understanding and more holistic perception of $360^\circ$ surrounding environment, which can naturally encode enriched scene context information compared to standard perspective image. Previous work has made lots of effort to solve the scene understanding task in a bottom-up form, thus each sub-task is processed separately and few correlations are explored in this procedure. In this paper, we propose a novel method using depth prior for holistic indoor scene understanding which recovers the objects' shapes, oriented bounding boxes and the 3D room layout simultaneously from a single panorama. In order to fully utilize the rich context information, we design a transformer-based context module to predict the representation and relationship among each component of the scene. In addition, we introduce a real-world dataset for scene understanding, including photo-realistic panoramas, high-fidelity depth images, accurately annotated room layouts, and oriented object bounding boxes and shapes. Experiments on the synthetic and real-world datasets demonstrate that our method outperforms previous panoramic scene understanding methods in terms of both layout estimation and 3D object detection.

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