2018/03/31 by Bo Xiong, Xiong, Bo, Kristen Grauman +1
Computer Science · #Advanced Image and Video Retrieval Techniques #Advanced Vision and Imaging #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Visual Attention and Saliency Detection #cs.CV
paper · pdf · doi:10.48550/arxiv.1804.00126
ECCV 2018
openalex publication_date 2018/03/31 · arxiv created 2018/08/12 · arxiv updated 2018/08/14 · openalex created_date 2025/10/10 · openalex updated_date 2026/07/28
360∘ panoramas are a rich medium, yet notoriously difficult to visualize in the 2D image plane. We explore how intelligent rotations of a spherical image may enable content-aware projection with fewer perceptible distortions. Whereas existing approaches assume the viewpoint is fixed, intuitively some viewing angles within the sphere preserve high-level objects better than others. To discover the relationship between these optimal snap angles and the spherical panorama's content, we develop a reinforcement learning approach for the cubemap projection model. Implemented as a deep recurrent neural network, our method selects a sequence of rotation actions and receives reward for avoiding cube boundaries that overlap with important foreground objects. We show our approach creates more visually pleasing panoramas while using 5x less computation than the baseline.