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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Differentiable Appearance Acquisition from a Flash/No-flash RGB-D Pair
Reconstructing 3D objects in natural environments requires solving the ill-posed problem of geometry, spatially-varying material, and lighting estimation. As such, many approaches impractically constrain to a dark environment, use controlled lighting rigs, or use few handheld captures but suffer reduced quality. We develop a method that uses just two smartphone exposures captured in ambient lighting to reconstruct appearance more accurately and practically than baseline methods. Our insight is that we can use a flash/no-flash RGB-D pair to pose an inverse rendering problem using point lighting. This allows efficient differentiable rendering to optimize depth and normals from a good initialization and so also the simultaneous optimization of diffuse environment illumination and SVBRDF material. We find that this reduces diffuse albedo error by 25%, specular error by 46%, and normal error by 30% against single and paired-image baselines that use learning-based techniques. Given that our approach is practical for everyday solid objects, we enable photorealistic relighting for mobile photography and easier content creation for augmented reality.
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- Award ID(s):
- 2144956
- NSF-PAR ID:
- 10421239
- Date Published:
- Journal Name:
- International Conference on Computational Photography
- Page Range / eLocation ID:
- 1 to 12
- 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
- Conference Paper:
- https://doi.org/10.1109/iccp54855.2022.9887646
