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Mobile Augmented Reality (AR) demands realistic rendering of virtual content that seamlessly blends into the physical environment. For this reason, AR headsets and recent smartphones are increasingly equipped with Time-of-Flight (ToF) cameras to acquire depth maps of a scene in real-time. ToF cameras are cheap and fast, however, they suffer from several issues that affect the quality of depth data, ultimately hampering their use for mobile AR. Among them, scale errors of virtual objects - appearing much bigger or smaller than what they should be - are particularly noticeable and unpleasant. This article specifically addresses these challenges by proposing InDepth, a real-time depth inpainting system based on edge computing. InDepth employs a novel deep neural network (DNN) architecture to improve the accuracy of depth maps obtained from ToF cameras. The DNN fills holes and corrects artifacts in the depth maps with high accuracy and eight times lower inference time than the state of the art. An extensive performance evaluation in real settings shows that InDepth reduces the mean absolute error by a factor of four with respect to ARCore DepthLab. Finally, a user study reveals that InDepth is effective in rendering correctly-scaled virtual objects, outperforming DepthLab.
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Mobile Maps Continue to Fail Pedestrians: Synthesised Reflective Auto-Aggro-Ethnographies of Walking
We consider mobile maps, the everyday smart-device-based programs that locate the user, provide insights into local space, and support wayfinding – or do they? The authors collectively reflect on past infuriating experiences with failures of mobile maps as pedestrians. We synthesise these thick descriptions, what we call reflective auto-aggro-ethnographies, to identify shortcomings in mobile maps: hidden verticality, missing local detail, incorrect sensor data, and poor pathing. We turn to human-centred design to point out how these shortcomings should be (or, rather, should have been) addressed.
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- PAR ID:
- 10596504
- Publisher / Repository:
- ACM
- Date Published:
- ISBN:
- 9798400713958
- Page Range / eLocation ID:
- 1 to 12
- Subject(s) / Keyword(s):
- Map interfaces pedestrians mobile GPS seamful design
- Format(s):
- Medium: X
- Location:
- Yokohama Japan
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript
- Conference Paper:
- https://doi.org/10.1145/3706599.3716217
