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LoGeR – 3D reconstruction from extremely long videos (DeepMind, UC Berkeley)
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Overview
LoGeR enables dense 3D reconstruction of extremely long videos by processing them in chunks with a hybrid memory module, addressing quadratic complexity issues. It utilizes Sliding Window Attention for local alignment and Test-Time Training to maintain global consistency, effectively reducing drift over sequences of up to 19,000 frames.
Summary from loger-project.github.io.
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loger-project.github.io
LoGeR – 3D reconstruction from extremely long videos (DeepMind, UC Berkeley)
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Hacker News · loger-project.github.io
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- Praise: The potential for 3D reconstruction from long videos could revolutionize applications like street-view mapping, enhancing the accuracy of visual data collection.
- Praise: This research represents a significant advancement in video to 3D reconstruction, indicating rapid progress in the field of AI and computer vision.
- Concern: There are serious concerns about the implications of this technology, particularly regarding its potential use in mass surveillance, which raises ethical questions.
- Concern: The accuracy of the model is questionable, as it may replicate lidar systems without providing reliable dimensional data, leading to potential inaccuracies.