An accurate understanding of omnidirectional environment lighting is crucial for high-quality virtual object rendering in mobile augmented reality (AR). In particular, to support reflective rendering, existing methods have leveraged deep learning models to estimate or have used physical light probes to capture physical lighting, typically represented in the form of an environment map. However, these methods often fail to provide visually coherent details or require additional setups. For example, the commercial framework ARKit uses a convolutional neural network that can generate realistic environment maps; however the corresponding reflective rendering might not match the physical environments. In this work, we present the design and implementation of a lighting reconstruction framework called LITAR that enables realistic and visually-coherent rendering. LITAR addresses several challenges of supporting lighting information for mobile AR. First, to address the spatial variance problem, LITAR uses two-field lighting reconstruction to divide the lighting reconstruction task into the spatial variance-aware near-field reconstruction and the directional-aware far-field reconstruction. The corresponding environment map allows reflective rendering with correct color tones. Second, LITAR uses two noise-tolerant data capturing policies to ensure data quality, namely guided bootstrapped movement and motion-based automatic capturing. Third, to handle the mismatch between the mobile computation capability and the high computation requirement of lighting reconstruction, LITAR employs two novel real-time environment map rendering techniques called multi-resolution projection and anchor extrapolation. These two techniques effectively remove the need of time-consuming mesh reconstruction while maintaining visual quality. Lastly, LITAR provides several knobs to facilitate mobile AR application developers making quality and performance trade-offs in lighting reconstruction. We evaluated the performance of LITAR using a small-scale testbed experiment and a controlled simulation. Our testbed-based evaluation shows that LITAR achieves more visually coherent rendering effects than ARKit. Our design of multi-resolution projection significantly reduces the time of point cloud projection from about 3 seconds to 14.6 milliseconds. Our simulation shows that LITAR, on average, achieves up to 44.1% higher PSNR value than a recent work Xihe on two complex objects with physically-based materials.
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Immersive Distributed Design Through Real-Time Capture, Translation, and Rendering of Three-Dimensional Mesh Data1
With design teams becoming more distributed, the sharing and interpreting of complex data about design concepts/prototypes and environments have become increasingly challenging. The size and quality of data that can be captured and shared directly affects the ability of receivers of that data to collaborate and provide meaningful feedback. To mitigate these challenges, the authors of this work propose the real-time translation of physical objects into an immersive virtual reality environment using readily available red, green, blue, and depth (RGB-D) sensing systems and standard networking connections. The emergence of commercial, off-the-shelf RGB-D sensing systems, such as the Microsoft Kinect, has enabled the rapid three-dimensional (3D) reconstruction of physical environments. The authors present a method that employs 3D mesh reconstruction algorithms and real-time rendering techniques to capture physical objects in the real world and represent their 3D reconstruction in an immersive virtual reality environment with which the user can then interact. Providing these features allows distributed design teams to share and interpret complex 3D data in a natural manner. The method reduces the processing requirements of the data capture system while enabling it to be portable. The method also provides an immersive environment in which designers can view and interpret the data remotely. A case study involving a commodity RGB-D sensor and multiple computers connected through standard TCP internet connections is presented to demonstrate the viability of the proposed method.
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- Award ID(s):
- 1650527
- NSF-PAR ID:
- 10137112
- Date Published:
- Journal Name:
- Journal of Computing and Information Science in Engineering
- Volume:
- 17
- Issue:
- 3
- ISSN:
- 1530-9827
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1115/1.4035001
