Desert ant increase the visibility of their nest entrances in the absence of landmarks

Desert ants have outstanding navigational skills. They live in the saltpans of North Africa, an extremely inhospitable environment. To find food for their nest mates, foraging ants have to walk far into the desert. Once they have found food, for example a dead insect, their actual problem begins: How do they find their way back to their nest as quickly as possible in the extremely hot and barren environment? "The desert ant Cataglyphis fortis stands out due its remarkable ability to successfully navigate and forage in even the harshest environments, making it an excellent subject for studying the intricacies of navigation. With an innate navigation mechanism called path integration, these ants use both a sun compass and a step counter to measure the distances they cover. In addition, they possess the ability to learn and utilize visible and olfactory cues. We believe that this extremely harsh habitat has led, during evolution, to a navigation system of unsurpassed precision," said Marilia Freire, the study's lead author, summarizing what is known so far about the amazing orientation skills of these small animals.

The scientists had noticed during previous studies in Tunisia that the nests in the center of the saltpans, where there are hardly any visible landmarks, had high mounds at the nest entrances. In contrast, nest hills near the shrub-covered edges of the saltpans were lower or barely noticeable. So the research team has wondered for some time if these visible differences serve a purpose in helping the ants better find their way home. "It's always hard to tell whether an animal does something on purpose or not. The high nest mounds in the middle of the saltpans could have been a side effect of differences in soil structure or wind conditions. However, crucial for our study was the idea to remove the mounds and to provide some nests with artificial landmarks and others not, and to observe what would happen," Markus Knaden, head of the Project Group Odor-guided Behavior in the Department of Evolutionary Neuroethology, explains the goal of the study.

For their experiments, the researchers followed the ants with a GPS device. This allowed them to track the ants on their way to the saltpan and back home. "We observed that desert ants are capable of traveling much greater distances than previously reported. The farthest distance a single animal traveled was more than two kilometers. However, we also observed an unexpectedly high mortality rate. About 20% of foraging ants do not find their way back home after extremely long runs and died in front of our eyes, which explains the enormous selection pressure for even better orientation," says Marilia Freire.

Experiments in which ants could be tracked with particular accuracy during the last meters to the nest, thanks to a grid painted on the floor, showed that the nest hills are important visual cues. If they were removed, fewer ants found their way back to the nest, while their nest mates simultaneously began to rebuild nest mounds as quickly as possible. If, on the other hand, the scientists placed artificial landmarks in the form of small black cylinders near the nest entrances whose mounds they had previously removed, the ants did not invest in building new ones. Apparently, the cylinders were sufficient for orientation.

In ant nests, labor is divided. Ants that go foraging are usually older and more experienced nest members, while younger ants are busy building. Therefore, there must be some kind of information flow between the two groups. The researchers do not yet know exactly how this is achieved. "One possibility would be that ants in the nest somehow notice that fewer foragers return home, and as a result, hill-building activities at the nest entrance are increased," says Marilia Freire.

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Tool transforms world landmark photos into 4D experiences

 Using publicly available tourist photos of world landmarks such as the Trevi Fountain in Rome or Top of the Rock in New York City, Cornell University researchers have developed a method to create maneuverable 3D images that show changes in appearance over time.

The method, which employs deep learning to ingest and synthesize tens of thousands of mostly untagged and undated photos, solves a problem that has eluded experts in computer vision for six decades.

"It's a new way of modeling scenes that not only allows you to move your head and see, say, the fountain from different viewpoints, but also gives you controls for changing the time," said Noah Snavely, associate professor of computer science at Cornell Tech and senior author of "Crowdsampling the Plenoptic Function," presented at the European Conference on Computer Vision, held virtually Aug. 23-28.

"If you really went to the Trevi Fountain on your vacation, the way it would look would depend on what time you went -- at night, it would be lit up by floodlights from the bottom. In the afternoon, it would be sunlit, unless you went on a cloudy day," Snavely said. "We learned the whole range of appearances, based on time of day and weather, from these unorganized photo collections, such that you can explore the whole range and simultaneously move around the scene."

Representing a place in a photorealistic way is challenging for traditional computer vision, partly because of the sheer number of textures to be reproduced. "The real world is so diverse in its appearance and has different kinds of materials -- shiny things, water, thin structures," Snavely said.

Another problem is the inconsistency of the available data. Describing how something looks from every possible viewpoint in space and time -- known as the plenoptic function -- would be a manageable task with hundreds of webcams affixed around a scene, recording data day and night. But since this isn't practical, the researchers had to develop a way to compensate.

"There may not be a photo taken at 4 p.m. from this exact viewpoint in the data set. So we have to learn from a photo taken at 9 p.m. at one location, and a photo taken at 4:03 from another location," Snavely said. "And we don't know the granularity of when these photos were taken. But using deep learning allows us to infer what the scene would have looked like at any given time and place."

The researchers introduced a new scene representation called Deep Multiplane Images to interpolate appearance in four dimensions -- 3D, plus changes over time. Their method is inspired in part on a classic animation technique developed by the Walt Disney Company in the 1930s, which uses layers of transparencies to create a 3D effect without redrawing every aspect of a scene.

"We use the same idea invented for creating 3D effects in 2D animation to create 3D effects in real-world scenes, to create this deep multilayer image by fitting it to all these disparate measurements from the tourists' photos," Snavely said. "It's interesting that it kind of stems from this very old, classic technique used in animation."

In the study, they showed that this model could be trained to create a scene using around 50,000 publicly available images found on sites such as Flickr and Instagram. The method has implications for computer vision research, as well as virtual tourism -- particularly useful at a time when few can travel in person.

"You can get the sense of really being there," Snavely said. "It works surprisingly well for a range of scenes."

First author of the paper is Cornell Tech doctoral student Zhengqi Li. Abe Davis, assistant professor of computer science in the Faculty of Computing and Information Science, and Cornell Tech doctoral student Wenqi Xian also contributed.

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