Eye Reflections Used to Reconstruct 3D Environments, Researchers Find

A team of researchers from the University of Maryland has made advancements in transforming eye reflections into distinguishable 3D scenes. Building upon Neural Radiance Fields (NeRF), an AI technology capable of reconstructing environments from 2D photographs, this new approach offers a glimpse into a potential future where a series of simple portrait photos can reveal detailed surroundings.

The process involved capturing subtle light reflections in human eyes using a sequence of images taken with a single sensor. By analyzing these reflections and calculating the direction of gaze in the photos, the team attempted to discern the immediate environment of the individual. Initially, high-resolution images were obtained from a fixed camera position, capturing a moving subject looking towards the camera. The researchers then focused on isolating and analyzing the reflections to reconstruct the scene.

The results, showcased in an animated set, present reasonably recognizable environmental reconstructions based on human eyes within a controlled setting. Additionally, a synthetic eye was used to capture a more impressive, dreamlike scene. However, when attempting to model eye reflections from music videos featuring Miley Cyrus and Lady Gaga, the researchers only obtained vague blobs, with their best guesses pointing towards an LED grid and a camera on a tripod. This highlights the considerable gap between the current technology and its practical application in real-world scenarios.

Overcoming significant challenges, the team managed to reconstruct crude and blurry scenes. One obstacle involved the cornea, which introduced inherent noise, making it difficult to separate the reflected light from the complex textures of the iris. To address this, the researchers employed cornea pose optimization to estimate the position and orientation of the cornea, along with iris texture decomposition to extract unique features specific to each individual’s iris. Furthermore, they utilized radial texture regularization loss, a machine-learning technique that simulates smoother textures, to enhance and isolate the reflected scenery.

Despite these breakthroughs and inventive solutions, substantial barriers remain. The authors acknowledge that their current real-world results are obtained under a “laboratory setup,” involving close-up captures of a person’s face, controlled lighting conditions, and deliberate movements. They emphasize that more challenging scenarios, such as video conferencing with natural head movements, present difficulties due to lower sensor resolution, dynamic range, and motion blur. Moreover, the team recognizes that their assumptions about iris texture might be overly simplistic, particularly considering that eyes typically rotate more extensively in unconstrained settings compared to the controlled environment in their study.

Nonetheless, the researchers view their progress as a significant milestone that can inspire future advancements. They hope that this work will encourage further explorations that leverage unexpected visual signals to unveil information about the surrounding world, thus broadening the horizons of 3D scene reconstruction. While more refined iterations of this technology may raise concerns about privacy intrusions, it is worth noting that the current version can only vaguely identify objects, such as a Kirby doll, even under the most ideal conditions.

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More about 3D environment reconstruction

• Neural Radiance Fields (NeRF)
• University of Maryland Department of Computer Science
• Tech Xplore article on the research
• 3D Scene Reconstruction
• Gaze Tracking Technology
• Augmented Reality