Add task category, link to paper

LICENSE.txt

@@ -0,0 +1,21 @@
+MIT License
+
+Copyright (c) 2024 Daniel McGann
+
+Permission is hereby granted, free of charge, to any person obtaining a copy
+of this software and associated documentation files (the "Software"), to deal
+in the Software without restriction, including without limitation the rights
+to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
+copies of the Software, and to permit persons to whom the Software is
+furnished to do so, subject to the following conditions:
+
+The above copyright notice and this permission notice shall be included in all
+copies or substantial portions of the Software.
+
+THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
+IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
+FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
+AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
+LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
+OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
+SOFTWARE.

README.md

@@ -1,12 +1,20 @@
 ---
 license: mit
 viewer: false
+task_categories:
+- robotics
 ---
+
 # TactileTracking: A tactile-based object tracking dataset
 [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
 
-We present a benchmark dataset for tactile-based object tracking, featuring 12 distinct objects and 84 tracking trials—7 trials per object, each lasting an average of 10.2 seconds. The dataset includes tactile video, per-frame 6DoF ground truth sensor poses, and pre-processed surface geometry constructed from each tactile video frame. For a robust, real-time, and accurate tactile-based object tracking solution, explore our work [NormalFlow](https://github.com/rpl-cmu/normalflow). To compare NormalFlow with other methods on this dataset,use the [NormalFlow Experiments](https://github.com/rpl-cmu/normalflow_experiment) repository.
+<div style="text-align: center;">
+    <video width="100%" onmouseover="this.pause()" onmouseout="this.play()" autoplay="" loop="" muted="">
+        <source src="https://joehjhuang.github.io/normalflow/videos/dataset.mp4" type="video/mp4">
+    </video>
+</div>
 
+We present a benchmark dataset for tactile-based object tracking, featuring 12 distinct objects and 84 tracking trials—7 trials per object, each lasting an average of 10.2 seconds. The dataset includes tactile video, per-frame 6DoF ground truth sensor poses, and pre-processed surface geometry constructed from each tactile video frame. For a robust, real-time, and accurate tactile-based object tracking solution, explore our work [NormalFlow](https://huggingface.co/papers/2412.09617). To compare NormalFlow with other methods on this dataset,use the [NormalFlow Experiments](https://github.com/rpl-cmu/normalflow_experiment) repository.
 
 ## Collection Setup
 
@@ -35,9 +43,6 @@ Each data collection trial directory contains the following components:
 - **contact_masks.npy**: An (N, H, W) array of the computed contact masks for each frame in `gelsight.mp4`, derived solely from the tactile images.
 - **gradient_maps.npy**: An (N, H, W, 2) array of the computed gradient maps for each frame in `gelsight.mp4`, based only on the tactile images.
 
-[TODO: Visualize the surface geometry information here]
-
-
 ## Dataset Statistics
 
 Our benchmark dataset focuses on frame-to-frame object pose tracking, with each trial ensuring overlap between the first (reference) frame and subsequent (target) frames. This setup restricts the object to local movement without long-distance shifts. The table below details the average 6DoF movement range for each object. This dataset prioritizes rotational movement, as excessive translational sliding risks damaging the sensor’s gel.