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Real-time incremental learning of scene light field with novel model and multiple agent with NSLF-OL

Real-time incremental learning of scene light field with novel model and multiple agent with NSLF-OL


NSLF-OL: Online Learning of Neural Surface Light Fields alongside Real-time Incremental 3D Reconstruction

arXiv paper abstract https://arxiv.org/abs/2305.00282



... novel view generation is ... important ... in ... graphics and ... human-robot interaction.


... This limits the usage ... in the robotics ... since robots (1) usually only capture a very small range of view directions to surfaces that cause arbitrary predictions on unseen, novel direction, (2) requires real-time algorithms, and (3) work with growing scenes, e.g., in robotic exploration.


... proposes a novel Neural Surface Light Fields model that copes with the small range of view directions while producing a good result in unseen directions ... training of ... model is highly efficient.


... design Multiple Asynchronous Neural Agents (MANA), a universal framework to learn each small region in parallel for large-scale growing scenes.


... model learns online the Neural Surface Light Fields (NSLF) aside from real-time 3D reconstruction with a sequential data stream as the shared input ... provides real-time rendering ... for visualization.


... showing the high flexibility to embed ... model into real-time 3D reconstruction and demonstrating high-fidelity view synthesis for these scenes ...



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