{"id":2,"date":"2024-04-29T19:18:50","date_gmt":"2024-04-29T19:18:50","guid":{"rendered":"https:\/\/mscvprojects.ri.cmu.edu\/2024team14\/?page_id=2"},"modified":"2024-12-06T00:42:30","modified_gmt":"2024-12-06T00:42:30","slug":"sample-page","status":"publish","type":"page","link":"https:\/\/mscvprojects.ri.cmu.edu\/2024team14\/","title":{"rendered":"Overview"},"content":{"rendered":"\n
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Presentation Slides<\/a><\/div>\n\n\n\n
Poster<\/a><\/div>\n\n\n\n
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A classical problem in Computer Vision is to infer a 3D scene representation from a limited number of images. Another challenge is to obtain accurate camera and pose information for the captured images. Finally, real-world applications demand real-time inference of 3D representations and the ability to render novel views at interactive speeds.<\/p>\n\n\n\n

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Sparse Input Views<\/p>\n<\/div>\n\n\n\n

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Unposed<\/p>\n<\/div>\n\n\n\n

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Generalizable to Unseen Categories<\/p>\n<\/div>\n<\/div>\n\n\n\n

Such a technology has widespread applications in domains such as autonomous driving, robotics, real estate, AR, VR, e-commerce, fashion, and so on.<\/p>\n\n\n\n

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Autonomous Driving<\/p>\n<\/div>\n\n\n\n

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Augmented Reality (AR)<\/p>\n<\/div>\n\n\n\n

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Real Estate Virtual Tours<\/p>\n<\/div>\n<\/div>\n\n\n\n

Thus, we propose a novel approach that, given a few RGB images of a previously unobserved scene with or without known poses, produces a 3D scene representation in a single feed-forward pass. Specifically, our method predicts 3D Gaussians that can be rendered from any novel view at interactive speeds.<\/p>\n\n\n

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A classical problem in Computer Vision is to infer a 3D scene representation from a limited number of images. Another challenge is to obtain accurate camera and pose information for the captured images. Finally, real-world applications demand real-time inference of 3D representations and the ability to render novel views at interactive speeds. Sparse Input Views … <\/p>\n

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