CleAR: Robust Context-Guided Generative Lighting Estimation for Mobile Augmented Reality

High-quality environment lighting is the foundation of creating immersive user experiences in mobile augmented reality (AR) applications. However, achieving visually coherent environment lighting estimation for Mobile AR is challenging due to several key limitations associated with AR device sensing capabilities, including limitations in device camera FoV and pixel dynamic ranges. Recent advancements in generative AI, which can generate high-quality images from different types of prompts, including texts and images, present a potential solution for high-quality lighting estimation. Still, to effectively use generative image diffusion models, we must address their key limitations of generation hallucination and slow inference process. To do so, in this work, we design and implement a generative lighting estimation system called CleAR that can produce high-quality and diverse environment maps in the format of 360$^\circ$ images. Specifically, we design a two-step generation pipeline guided by AR environment context data to ensure the results follow physical environment visual context and color appearances. To improve the estimation robustness under different lighting conditions, we design a real-time refinement component to adjust lighting estimation results on AR devices. To train and test our generative models, we curate a large-scale environment lighting estimation dataset with diverse lighting conditions. Through quantitative evaluation and user study, we show that CleAR outperforms state-of-the-art lighting estimation methods on both estimation accuracy and robustness. Moreover, CleAR supports real-time refinement of lighting estimation results, ensuring robust and timely environment lighting updates for AR applications. Our end-to-end generative estimation takes as fast as 3.2 seconds, outperforming state-of-the-art methods by 110x.

RoVaR: Robust Multi-agent Tracking through Dual-layer Diversity in Visual and RF Sensor Fusion

The plethora of sensors in our commodity devices provides a rich substrate for sensor-fused tracking. Yet, today's solutions are unable to deliver robust and high tracking accuracies across multiple agents in practical, everyday environments - a feature central to the future of immersive and collaborative applications. This can be attributed to the limited scope of diversity leveraged by these fusion solutions, preventing them from catering to the multiple dimensions of accuracy, robustness (diverse environmental conditions) and scalability (multiple agents) simultaneously. In this work, we take an important step towards this goal by introducing the notion of dual-layer diversity to the problem of sensor fusion in multi-agent tracking. We demonstrate that the fusion of complementary tracking modalities, - passive/relative (e.g., visual odometry) and active/absolute tracking (e.g., infrastructure-assisted RF localization) offer a key first layer of diversity that brings scalability while the second layer of diversity lies in the methodology of fusion, where we bring together the complementary strengths of algorithmic (for robustness) and data-driven (for accuracy) approaches. RoVaR is an embodiment of such a dual-layer diversity approach that intelligently attends to cross-modal information using algorithmic and data-driven techniques that jointly share the burden of accurately tracking multiple agents in the wild. Extensive evaluations reveal RoVaR's multi-dimensional benefits in terms of tracking accuracy (median of 15cm), robustness (in unseen environments), light weight (runs in real-time on mobile platforms such as Jetson Nano/TX2), to enable practical multi-agent immersive applications in everyday environments.