Low-cost 3D scanners and automatic photogrammetry software have brought digitization of objects into 3D models to the level of the consumer. However, the digitization techniques are either tedious, disruptive to the scanned object, or expensive. We create a novel 3D scanning system using consumer grade hardware that revolves a camera around the object of interest. Our approach does not disturb the object during capture and allows us to scan delicate objects that can deform under motion, such as potted plants. Our system consists of a Raspberry Pi camera and computer, stepper motor, 3D printed camera track, and control software. Our 3D scanner allows the user to gather image sets for 3D model reconstruction using photogrammetry software with minimal effort. We scale 3D scanning to objects of varying sizes by designing our scanner using programmatic modeling, and allowing the user to change the physical dimensions of the scanner without redrawing each part.
BioFace-3D: continuous 3d facial reconstruction through lightweight single-ear biosensors
Over the last decade, facial landmark tracking and 3D reconstruction have gained considerable attention due to their numerous applications such as human-computer interactions, facial expression analysis, and emotion recognition, etc. Traditional approaches require users to be confined to a particular location and face a camera under constrained recording conditions (e.g., without occlusions and under good lighting conditions). This highly restricted setting prevents them from being deployed in many application scenarios involving human motions. In this paper, we propose the first single-earpiece lightweight biosensing system, BioFace-3D, that can unobtrusively, continuously, and reliably sense the entire facial movements, track 2D facial landmarks, and further render 3D facial animations. Our single-earpiece biosensing system takes advantage of the cross-modal transfer learning model to transfer the knowledge embodied in a high-grade visual facial landmark detection model to the low-grade biosignal domain. After training, our BioFace-3D can directly perform continuous 3D facial reconstruction from the biosignals, without any visual input. Without requiring a camera positioned in front of the user, this paradigm shift from visual sensing to biosensing would introduce new opportunities in many emerging mobile and IoT applications. Extensive experiments involving 16 participants under various settings demonstrate that BioFace-3D can accurately track 53 major facial more »
- Award ID(s):
- 2132112
- Publication Date:
- NSF-PAR ID:
- 10377880
- Journal Name:
- Proceedings of the 27th Annual International Conference on Mobile Computing and Networking (MobiCom '21)
- Page Range or eLocation-ID:
- 350 to 363
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Publisher's Version of Record
- https://doi.org/10.1145/3447993.3483252
