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better images
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jonbarron committed Mar 24, 2021
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6 changes: 3 additions & 3 deletions mipnerf/index.html
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Expand Up @@ -107,7 +107,7 @@ <h2 class="col-md-12 text-center">
<div class="col-md-4 col-md-offset-4 text-center">
<ul class="nav nav-pills nav-justified">
<li>
<a href="https://arxiv.org/TODO">
<a href="mipnerf.pdf">
<image src="img/mip_paper_image.jpg" height="60px">
<h4><strong>Paper</strong></h4>
</a>
Expand Down Expand Up @@ -169,7 +169,7 @@ <h3>
Typical positional encoding (as used in Transformer networks and Neural Radiance Fields) maps a single point in space to a feature vector, where each element is generated by a sinusoid with an exponentially increasing frequency:
</p>
<p style="text-align:center;">
<image src="img/pe_seq_eqn.png" height="50px" class="center">
<image src="img/pe_seq_eqn_pad.png" height="50px" class="img-responsive">
</p>
<video id="v0" width="100%" autoplay loop muted>
<source src="img/pe_anim_horiz.mp4" type="video/mp4" />
Expand All @@ -180,7 +180,7 @@ <h3>
Our <em>integrated positional encoding</em> considers Gaussian <em>regions</em> of space, rather than infinitesimal points. This provides a natural way to input a "region" of space as query to a coordinate-based neural network, allowing the network to reason about sampling and aliasing. The expected value of each positional encoding component has a simple closed form:
</p>
<p style="text-align:center;">
<image src="img/ipe_eqn_under.png" height="30px" class="center">
<image src="img/ipe_eqn_under_pad.png" height="30px" class="img-responsive">
</p>
<video id="v0" width="100%" autoplay loop muted>
<source src="img/ipe_anim_horiz.mp4" type="video/mp4" />
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