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<!doctype html>
<html lang="en">
<head>
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<meta property="og:description" content="Paper description."/>
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<header class="container">
<hr>
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<div class="container text-center">
<a class="blog-header-logo text-body-emphasis" href="#">SketchSampler: Sketch-based 3D Reconstruction <br>via View-dependent Depth Sampling</a>
</div>
<hr>
<!-- author list -->
<div class="container text-center">
<div class="row">
<div class="col"><p class="fs-5">Chenjian Gao<sup>1</sup></p></div>
<div class="col"><p class="fs-5"><a href="https://yuqian1023.github.io/" class="link-body-emphasis">Qian Yu</a><sup>1</sup></p></div>
<div class="col"><p class="fs-5"><a href="https://lucassheng.github.io/" class="link-body-emphasis">Lu Sheng</a><sup>1</sup></p></div>
<div class="col"><p class="fs-5"><a href="http://personal.ee.surrey.ac.uk/Personal/Y.Song/" class="link-body-emphasis">Yi-Zhe Song</a><sup>2</sup></p></div>
<div class="col"><p class="fs-5">Dong Xu<sup>3</sup></p></div>
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<!-- author organization -->
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<p class="fs-6"><sup>1</sup>Beihang University <sup>2</sup>University of Surrey</p> <sup>3</sup>The University of Hong Kong</p>
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<h3>ECCV 2022</h3>
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<h3>Paper</h3>
</a>
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<div class="col col-lg-3">
<a class="icon-link icon-link-hover" style="--bs-icon-link-transform: translate3d(0, -.125rem, 0);"
href="https://github.com/cjeen/sketchsampler">
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<path d="M8 0C3.58 0 0 3.58 0 8c0 3.54 2.29 6.53 5.47 7.59.4.07.55-.17.55-.38 0-.19-.01-.82-.01-1.49-2.01.37-2.53-.49-2.69-.94-.09-.23-.48-.94-.82-1.13-.28-.15-.68-.52-.01-.53.63-.01 1.08.58 1.23.82.72 1.21 1.87.87 2.33.66.07-.52.28-.87.51-1.07-1.78-.2-3.64-.89-3.64-3.95 0-.87.31-1.59.82-2.15-.08-.2-.36-1.02.08-2.12 0 0 .67-.21 2.2.82.64-.18 1.32-.27 2-.27.68 0 1.36.09 2 .27 1.53-1.04 2.2-.82 2.2-.82.44 1.1.16 1.92.08 2.12.51.56.82 1.27.82 2.15 0 3.07-1.87 3.75-3.65 3.95.29.25.54.73.54 1.48 0 1.07-.01 1.93-.01 2.2 0 .21.15.46.55.38A8.012 8.012 0 0 0 16 8c0-4.42-3.58-8-8-8z"/>
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<h3>Code</h3>
</a>
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</div>
<br>
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<div class="text-center">
<figure class="figure">
<img src="./assets/teaser.png" class="img-thumbnail figure-img img-fluid rounded" alt="teaser">
<!-- <figcaption class="figure-caption">Sketch-to-pointcloud generation results of our proposed SketchSampler.</figcaption> -->
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<!-- Abstract -->
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<h2 class="card-title text-center card-title-margin">Abstract</h2>
<p class="card-text">
Reconstructing a 3D shape based on a single sketch image is challenging due to the large domain gap
between a sparse, irregular sketch and a regular, dense 3D shape. Existing works try to employ the
global feature extracted from sketch to directly predict the 3D coordinates, but they usually suffer
from losing fine details that are not faithful to the input sketch. Through analyzing the 3D-to-2D
projection process, we notice that the density map that characterizes the distribution of 2D point
clouds(i.e., the probability of points projected at each location of the projection plane) can be used
as a proxy to facilitate the reconstruction process. To this end, we first translate a sketch via an
image translation network to a more informative 2D representation that can be used to generate a density
map. Next, a 3D point cloud is reconstructed via a two-stage probabilistic sampling process: first
recovering the 2D points(i.e., the x and y coordinates) by sampling the density map; and then predicting
the depth(i.e., the z coordinate) by sampling the depth values at the ray determined by each 2D point.
Extensive experiments are conducted, and both quantitative and qualitative results show that our proposed
approach significantly outperforms other baseline methods.
</p>
</div>
</div>
<br>
<!-- Methodology -->
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<h2 class="card-title card-title-margin">Methodology</h2>
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<figure class="figure">
<img src="./assets/method_diagram.png" class="figure-img img-fluid rounded" alt="...">
<!-- <figcaption class="figure-caption">A caption for the above image.</figcaption> -->
</figure>
</div>
<p class="card-text">
our sketch-based modeling framework mainly consists of two components: a sketch translator and a point cloud
generator. Given an input sketch I, the sketch translator first translates it to a feature map F . Next, the
point cloud generator produces a point cloud S based on the given feature map F . When recovering the point cloud,
a 2D density map is first predicted, from which 2D points are sampled; then the depth of each 2D point is predicted
by using the proposed conditional depth generator.
</p>
</div>
</div>
<br>
<!-- Citation -->
<div class="card">
<div class="card-body">
<h2 class="card-title card-title-margin">Citation</h2>
<p id="bibtex" class="card-text" style="border: #1a1c20 1px">
<code> @inproceedings{gaosketchsampler,<br>
title={SketchSampler: Sketch-based 3D Reconstruction via View-dependent Depth Sampling},<br>
author={Gao, Chenjian and Yu, Qian and Sheng, Lu and Song, Yi-Zhe and Xu, Dong},<br>
booktitle={European Conference on Computer Vision},<br>
year={2022}<br>
}
</code>
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<h4 class="card-title card-title-margin">Acknowledgements</h4>
<p class="card-text">We thank Ximing Xing for providing us with the source code of the web page to help us
build the project home page.</p>
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