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360(o) Camera Alignment via Segmentation

Panoramic 360º images taken under unconstrained conditions present a significant challenge to current state-of-the-art recognition pipelines, since the assumption of a mostly upright camera is no longer valid. In this work, we investigate how to solve this problem by fusing purely geometric cues, such as apparent vanishing points, with learned semantic cues, such as the expectation that some visual elements (e.g. doors) have a natural upright position. We train a deep neural network to leverage these cues to segment the image-space endpoints of an imagined “vertical axis”, which is orthogonal to the ground plane of a scene, thus levelling the camera. We show that our segmentation-based strategy significantly increases performance, reducing errors by half, compared to the current state-of-the-art on two datasets of 360º imagery. We also demonstrate the importance of 360º camera levelling by analysing its impact on downstream tasks, finding that incorrect levelling severely degrades the performance of real-world computer vision pipelines.

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