![]() ![]() In testing the method on a human eye, a very modest resolution rendering of the image is seen, but in a depth-mapped 3D rendering. The imaged person was asked to move about within the camera's field of view as multiple images were captured. Despite subtle inaccuracies in cornea location and geometry estimates, the method was effective in scene reconstruction.Īrea lights placed by the person's sides (out of frame) were used to illuminate the object of interest in front of them. ![]() This also allows the camera's angle to be determined, plotting the coordinates of images over the curved geometry and setting a viewing direction for the NeRF AI to use later to reconstruct the 3D rendering. To remove the iris from the images, texture decomposition was performed by training a 2D texture map that learns the iris texture and deletes it.Įxploiting cornea geometry, which is approximately the same across all adults, computations were made to track exactly where their eyes are looking. Within the image are all sorts of artifacts of the eye, the complexity of iris textures, and the identifiable yet low-resolution reflections captured in each image. Zooming in on the reflection in the imaged person's eye, a mirror image of the field of view is visible, and objects in the area are identifiable. In the current effort by the Maryland team, they start with multiple images from a high-resolution camera in a fixed position, focused on an individual in motion looking towards the camera, framed much as a passport or driver's license photo might be. Typically with a few dozen still images at different angles, NeRF can generate a 3D representation with enough depth and detail to be almost indistinguishable from a video that can move around an object or space. In a paper on the pre-print server arXiv, titled "Seeing the World through Your Eyes," the team describes the methods used to capture the eye reflections and transform them into coherent 3D renderings using a specially trained AI visual rendering algorithm called NeRF.Ī neural radiance field (NeRF) is an AI neural network that can generate novel continuous views of complex 3D scenes based on multiple 2D images. There you'll find articles covering tips and how-to's, Audubon's ethical guidelines for wildlife photography, and gear recommendations.Researchers at the University of Maryland were able to capture this reflected light and extract a three-dimensional model of the surroundings. If so, our photography section is a good place to get started. The images also illustrate the many different techniques and approaches used by wildlife photographers, which you can read about in the detailed “behind the shot” stories for each photograph.Īfter perusing this gallery, you might feel inspired to pick up a camera and try your own hand at avian photography. Shared in no particular order, these shots show birds from around the world in all of their breathtaking variety and wonder. So here are 100 more of our favorite photos for your enjoyment. Be sure to check them out if you haven't already.īut as always, with so many amazing submissions, we couldn't stop there. Then the hard part began: After reviewing every anonymous image and video file, three panels of expert judges selected just 13 winners and honorable mentions. For the 14 installment of our annual competition, we had more than 2,200 individuals from across the United States and Canada submit almost 9,000 photographs and videos. Another year, another fantastic Audubon Photography Awards. ![]()
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