This work presents a novel framework for web-based environment-aware rendering and interaction in augmented reality based on WebXR and three.js. It aims at accelerating the development of device-agnostic Augmented Reality (AR) applications. The solution allows for a realistic rendering of 3D elements, handles geometry occlusion, casts shadows of virtual objects onto real surfaces, and provides physics interaction with real-world objects. Unlike most existing state-of-the-art systems that are built to run on a specific hardware configuration, the proposed solution targets the web environment and is designed to work on a vast range of devices and configurations. Our solution can use monocular camera setups with depth data estimated by deep neural networks or, when available, use higher-quality depth sensors (e.g., LIDAR, structured light) that provide a more accurate perception of the environment. To ensure consistency in the rendering of the virtual scene a physically based rendering pipeline is used, in which physically correct attributes are associated with each 3D object, which, combined with lighting information captured by the device, enables the rendering of AR content matching the environment illumination. All these concepts are integrated and optimized into a pipeline capable of providing a fluid user experience even on middle-range devices. The solution is distributed as an open-source library that can be integrated into existing and new web-based AR projects. The proposed framework was evaluated and compared in terms of performance and visual features with two state-of-the-art alternatives.

Fiducial Markers are heavily used for pose estimation in many applications from robotics to augmented reality. In this paper we present an algorithm for the detection of aruco marker at larger distance. The algorithm uses the quadrilateral sum conjecture and analyzes the sum of the cosine of the internal angles to detect squares at larger distances. Experiments conducted showed that the developed solution was able to improve the detection distance when compared to other methods that use similar marker while keeping similar pose estimation precision.

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