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* add tensorf intro video * add tensorf images * update images to adapt dark mode * add documentation for TensoRF * grammer check * add some anotations --------- Co-authored-by: Matthew Tancik <matt@lumalabs.ai>
34 lines
1.5 KiB
Markdown
34 lines
1.5 KiB
Markdown
# TensoRF
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<h4>Tensorial Radiance Fields</h4>
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```{button-link} https://apchenstu.github.io/TensoRF/
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:color: primary
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:outline:
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Paper Website
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```
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<video id="teaser" muted autoplay playsinline loop controls width="100%">
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<source id="mp4" src="https://apchenstu.github.io/TensoRF/video/train_process.mp4" type="video/mp4">
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</video>
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### Running Model
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```bash
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ns-train tensorf
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```
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## Overview
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```{image} imgs/tensorf/models_tensorf_pipeline.png
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:align: center
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```
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TensoRF models the radiance field of a scene as a 4D tensor, which represents a 3D voxel grid with per-voxel multi-channel features. TensoRF factorizes the 4D scene tensor into multiple compact low-rank tensor components using CP or VM modes. CP decomposition factorizes tensors into rank-one components with compact vectors. Vector-Matrix (VM) decomposition factorizes tensors into compact vector and matrix factors.
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```{image} imgs/tensorf/models_tensorf_factorization.png
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:align: center
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```
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TensoRF with CP(left) and VM(right) decompositions results in a significantly reduced memory footprint compared to previous and concurrent works that directly optimize per-voxel features, such as [Plenoxels](https://alexyu.net/plenoxels/) and [PlenOctrees](https://alexyu.net/plenoctrees/). In experiments, TensoRF with CP decomposition achieves fast reconstruction with improved rendering quality and a smaller model size compared to NeRF. Furthermore, TensoRF with VM decomposition enhances rendering quality even further, while reducing reconstruction time and maintaining a compact model size.
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