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  2. README.md +6 -2
LICENSE.txt ADDED
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+ MIT License
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+
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+ Copyright (c) 2024 Daniel McGann
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+
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+ Permission is hereby granted, free of charge, to any person obtaining a copy
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+ of this software and associated documentation files (the "Software"), to deal
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+ in the Software without restriction, including without limitation the rights
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+ to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
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+ copies of the Software, and to permit persons to whom the Software is
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+ furnished to do so, subject to the following conditions:
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+
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+ The above copyright notice and this permission notice shall be included in all
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+ copies or substantial portions of the Software.
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+
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+ THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
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+ IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
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+ FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
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+ AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
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+ LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
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+ OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE
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+ SOFTWARE.
README.md CHANGED
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  # TactileTracking: A tactile-based object tracking dataset
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  [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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  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.
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  - **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.
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  - **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.
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- [TODO: Visualize the surface geometry information here]
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-
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  ## Dataset Statistics
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  # TactileTracking: A tactile-based object tracking dataset
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  [![License: MIT](https://img.shields.io/badge/License-MIT-yellow.svg)](https://opensource.org/licenses/MIT)
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+ <div style="text-align: center;">
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+ <video width="100%" onmouseover="this.pause()" onmouseout="this.play()" autoplay="" loop="" muted="">
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+ <source src="https://joehjhuang.github.io/normalflow/videos/dataset.mp4" type="video/mp4">
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+ </video>
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+ </div>
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+
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  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.
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  - **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.
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  - **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.
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  ## Dataset Statistics
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