Add link to Tokyo photograph license

docs/index.html

@@ -333,10 +333,10 @@ figure {
 				<source src="assets/tokyo_online_training_counter.mp4" type="video/mp4">
 				Your browser does not support the video tag.
 			</video>
-			<p class="caption">
-				Real-time training progress on the image task where the neural network learns the mapping from 2D coordinates to RGB colors of a high-resolution image. Note that in this video, the network is trained from scratch - but converges so quickly you may miss it if you blink! <br/>
-			</p>
 		</figure>
+		<p class="caption_justify">
+			Real-time training progress on the image task where the neural network learns the mapping from 2D coordinates to RGB colors of a high-resolution image. Note that in this video, the network is trained from scratch - but converges so quickly you may miss it if you blink! <br/>
+		</p>
 
 		<h3>Neural Radiance Fields</h3>
 		<hr>
@@ -437,10 +437,10 @@ figure {
 				<source src="assets/sdf_grid_lq.mp4" type="video/mp4">
 				Your browser does not support the video tag.
 			</video>
-			<p class="caption">
-				Real-time training progress on various SDF datsets. Training data is generated on the fly from the ground-truth mesh using the <a href="https://developer.nvidia.com/optix">NVIDIA OptiX raytracing framework</a>.
-			</p>
 		</figure>
+		<p class="caption_justify">
+			Real-time training progress on various SDF datsets. Training data is generated on the fly from the ground-truth mesh using the <a href="https://developer.nvidia.com/optix">NVIDIA OptiX raytracing framework</a>.
+		</p>
 
 		<h3>Neural Radiance Cache</h3>
 		<hr>
@@ -449,10 +449,10 @@ figure {
 				<source src="assets/nrc_new_vs_old.mp4" type="video/mp4">
 				Your browser does not support the video tag.
 			</video>
-			<p class="caption">
-				Direct visualization of a <em>neural radiance cache</em>, in which the network predicts outgoing radiance at the first non-specular vertex of each pixel's path, and is trained on-line from rays generated by a real-time pathtracer. On the left, we show results using the triangle wave encoding of <a href="https://research.nvidia.com/publication/2021-06_Real-time-Neural-Radiance">[Müller et al. 2021]</a>; on the right, the new multiresolution hash encoding allows the network to learn much sharper details, for example in the shadow regions.
-			</p>
 		</figure>
+		<p class="caption_justify">
+			Direct visualization of a <em>neural radiance cache</em>, in which the network predicts outgoing radiance at the first non-specular vertex of each pixel's path, and is trained on-line from rays generated by a real-time pathtracer. On the left, we show results using the triangle wave encoding of <a href="https://research.nvidia.com/publication/2021-06_Real-time-Neural-Radiance">[Müller et al. 2021]</a>; on the right, the new multiresolution hash encoding allows the network to learn much sharper details, for example in the shadow regions.
+		</p>
 
 	</section>
 
@@ -514,9 +514,11 @@ figure {
 			<br/>
 			<em>Girl With a Pearl Earing</em> renovation by Koorosh Orooj <a href="http://profoundism.com/free_licenses.html">(CC BY-SA 4.0 License)</a>
 			<br/>
+			<em>Tokyo</em> gigapixel photograph by Trevor Dobson <a href="https://creativecommons.org/licenses/by-nc-nd/2.0/">(CC BY-NC-ND 2.0 License)</a>
+			<br/>
 			<em>Lucy</em> model from the <a href="http://graphics.stanford.edu/data/3Dscanrep/">Stanford 3D scan repository</a>
 			<br/>
-			<em>Factory robot dataset by Arman Toornias and Saurabh Jain.</em>
+			<em>Factory robot</em> dataset by Arman Toornias and Saurabh Jain.
 			<br/>
 			<em>Disney Cloud</em> model by Walt Disney Animation Studios. (<a href="https://media.disneyanimation.com/uploads/production/data_set_asset/6/asset/License_Cloud.pdf">CC BY-SA 3.0</a>)
 			<br/>