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<!DOCTYPE html>
<html>
<head>
<title>Multi-task View Synthesis with Neural Radiance Fields</title>
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<h1 class="title is-1 publication-title">Multi-task View Synthesis with Neural Radiance Fields</h1>
<h4 class="title is-4">ICCV 2023</h4>
<div class="is-size-5 publication-authors">
<span class="author-block">
<a href="https://zsh2000.github.io/">Shuhong Zheng</a><sup>1,*</sup>,</span>
<span class="author-block">
<a href="https://zpbao.github.io/">Zhipeng Bao</a><sup>2,*</sup>,</span>
<span class="author-block">
<a href="http://www.cs.cmu.edu/~hebert/">Martial Hebert</a><sup>2</sup>,
</span>
<span class="author-block">
<a href="https://yxw.web.illinois.edu/">Yu-Xiong Wang</a><sup>1</sup>
</span>
</div>
<div class="is-size-5 publication-authors">
<span class="author-block"><sup>1</sup>University of Illinois Urbana-Champaign,</span>
<span class="author-block"><sup>2</sup>Carnegie Mellon University</span>
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<span>arXiv</span>
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</section>
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<h2 class="subtitle has-text-centered">
Our MuvieNeRF compared with conventional discriminative multi-task learning method.
</h2>
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<h2 class="title is-3">Abstract</h2>
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<p>
Multi-task visual learning is a critical aspect of computer vision.
Current research predominantly concentrates on the multi-task dense prediction setting,
which overlooks the intrinsic 3D world and its multi-view consistent structures, and lacks the
capacity for versatile imagination.
</p>
<p>
To address these limitations, we present a novel problem setting -- multi-task view synthesis (MTVS),
which reinterprets multi-task prediction as a set of novel-view synthesis tasks for multiple scene properties, including RGB.
To tackle the MTVS problem, we propose MuvieNeRF, a framework that incorporates both multi-task and cross-view knowledge to simultaneously
synthesize multiple scene properties. \modelname integrates two key modules, the Cross-Task Attention (CTA) and Cross-View Attention (CVA) modules,
enabling the efficient use of information across multiple views and tasks.
</p>
<p>
Extensive evaluations on both synthetic and realistic benchmarks demonstrate that MuvieNeRF is capable of simultaneously
synthesizing different scene properties with promising visual quality, even outperforming conventional discriminative models in various settings.
Notably, we show that MuvieNeRF exhibits universal applicability across a range of NeRF backbones.
</p>
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MuvieNeRF is a unified framework for multi-task view synthesis equipped with Cross-View Attention (CVA)
and Cross-Task Attention (CTA) modules. It predicts multiple scene properties for arbitrary 3D coordinates with nearby-view annotations.
<div align="center"><img src="./static/images/pipeline_web.png" alt="" width=90% /></span></div>
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<h2 class="title is-3">Experimental Results</h2>
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<h3 class="title is-4">Comparison with Baselines</h3>
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<p>
We conduct experiments on two datasets: <a href="https://arxiv.org/abs/1906.05797">Replica</a> and <a href="https://openaccess.thecvf.com/content_ICCV_2017/papers/McCormac_SceneNet_RGB-D_Can_ICCV_2017_paper.pdf">SceneNet RGB-D</a>.
Five tasks beyond RGB are chosen: surface normal prediction (SN),
shading prediction (SH), edge detection (ED), keypoint detection (KP) and semantic label prediction (SL). Baseline methods include the state-of-the-art discriminative model
<a href="https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136870506.pdf">InvPT</a>, single-task NeRF model
<a href="https://shuaifengzhi.com/Semantic-NeRF/">Semantic-NeRF</a> and naive multi-task NeRF model
<a href="https://openaccess.thecvf.com/content/WACV2023/papers/Zhang_Beyond_RGB_Scene-Property_Synthesis_With_Neural_Radiance_Fields_WACV_2023_paper.pdf">SS-NeRF</a>.
</p>
<p>
Qualitative comparison on Replica dataset:
</p>
<div align="center"><img src="./static/images/replica.png" alt="" width=85% /></span></div>
<p>
Qualitative comparison on SceneNet RGB-D dataset:
</p>
<div align="center"><img src="./static/images/scenenet.png" alt="" width=70% /></span></div>
</div>
<h3 class="title is-4">Out-of-distribution Generalization</h3>
<div class="content has-text-justified">
<p>
The knowledge of multi-task synergy learned during training benefits generalization on out-of-distribution datasets, boosting the performance of
novel view RGB synthesis, even when 2D task signals from input views are unavailable.
</p>
<p>
Four out-of-distribution datasets <a href="http://www.scan-net.org/">ScanNet</a>,
<a href="https://theairlab.org/tartanair-dataset/">TartanAir</a>,
<a href="https://openaccess.thecvf.com/content_CVPR_2020/papers/Yao_BlendedMVS_A_Large-Scale_Dataset_for_Generalized_Multi-View_Stereo_Networks_CVPR_2020_paper.pdf">BlendedMVS</a>,
<a href="https://bmild.github.io/llff/">LLFF</a> (from left to right, top to bottom) are evaluated:
</p>
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<section class="section" id="BibTeX">
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<h2 class="title">BibTeX</h2>
<pre><code>@inproceedings{zheng2023mtvs,
title={Multi-task View Synthesis with Neural Radiance Fields},
author={Zheng, Shuhong and Bao, Zhipeng and Hebert, Martial and Wang, Yu-Xiong},
booktitle={ICCV},
year={2023}
}</code></pre>
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