By borrowing a trick from tiny jumping spiders, Northwestern University engineers have developed an extremely energy-efficient 3D camera. Called SpiderCam, the new device senses depth the same way that jumping spiders judge distances before making a high precision hop. To estimate depth, the system captures two images of the same scene with slightly different focus settings and measures subtle differences in blurriness between the two images. With this strategy, the camera produces real-time 3D maps while consuming less than a watt of power. That’s less energy than used by a standard nightlight. The innovation could enable a new generation of battery-powered devices that need to gauge their surroundings, like wearable technologies, assistive devices, robots and drones. The study’s co-first authors Marcos Ferreira and Tianao Li presented the work on June 7 at the Computer Vision Foundation’s Conference on Computer Vision and Pattern Recognition in Denver “Jumping spiders jump to catch prey, to avoid predators and to get around, and that requires excellent vision,” said Northwestern’s Emma Alexander, the study’s corresponding author. “But their brains are very small — the size of a poppy seed — so they have to compute these distances in a highly efficient way. We wanted to understand whether we could borrow some of the same principles to create an extremely energy efficient depth sensor that could be used in resource-constrained situations where users don’t have unlimited access to power.” The innovation could enable a new generation of devices that need to gauge their surroundings, like wearable technologies, robots and drones. An expert in bio-inspired computer vision, Alexander is an assistant professor of computer science at Northwestern’s McCormick School of Engineering. Most 3D cameras estimate depth either by comparing images from multiple viewpoints or by projecting and measuring light. While these approaches work well, they can require