5 Computer Vision News Computer Vision News image was generated by this differentiable, fully-antialiased renderer, even though it wasn’t. We then compute the gradients for the micro-edges as if the image had been rendered this way— without actually modifying the image itself. While the method is rooted in computer graphics techniques, it is designed to serve core computer vision tasks like 3D reconstruction. The algorithm optimizes a model for reconstruction using a collection of images of a static or dynamic scene. “This is inverse computer graphics,” Stan notes. “We go from camera images to the model. To do that, we build a rendering pipeline and can then backpropagate the gradients to optimize the model for reconstruction. I showed a scene of a monkey, one of my daughter’s favorite toys, during my oral presentation. I took around 500 pictures of it in my backyard and ran COLMAP on it to get the extrinsics of the cameras. Then, just just from this collection of images, I can optimize the mesh and the texture and get a model of the toy!” Another groundbreaking aspect of this work is its novel approach to handling geometry intersections in rasterization – the first method of its kind, as far as Stan is aware. “I didn’t find anyone attempting to handle geometry intersections in the literature,” he reveals. “I understand why. In most cases, it’s challenging, and for static scenes, it’s not really necessary, but it can be very necessary for dynamic scenes!” Rasterized Edge Gradients UKRAINE CORNER
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