10 DAILY CVPR Saturday Despite their advancements, Riku and Eric acknowledge that there are still challenges to overcome. In particular, the model assumes static scenes, meaning everything in the environment must be stationary. Eric notes that while their system is one of the more robust options available for single-camera SLAM, it is not infallible. “There are issues with dynamics and working in a variety of scenes, like super large-scale, outdoor scenes, where it's still not perfect,” he reveals. “Also, we can run it on movie clips, and it works well, but we can’t just feed in a whole movie.” Managing real-time performance in SLAM is another challenge the pair encountered. Eric explains that they wanted to see what was happening live on their desktop as they moved the camera. “One of our key contributions was this prior that puts everything in 3D,” he tells us. “We can also imagine that as a set of rays from a camera. That gave us the benefit that we could keep the camera model general so that we could handle things that typical monocular SLAM systems can’t, like zooming in on a video!” One way they pushed the real-time aspect of the project was by finding ways to implement some general form of projective correspondence. There are other ways of matching, such as in pixel space or 3D space, but they found that these methods were a bottleneck to real-time performance and degraded the system's accuracy. Looking ahead, Riku envisions future work that addresses the limitations of static scene assumptions. “We need to handle dynamic scenes better, as they’re everywhere in the real world,” he adds. “Humans are moving, cars are moving, and assuming static scenes has always been a limitation of typical SLAM systems. Many new works try to Highlight Presentation
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