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doc/data_processing_single_scene.md

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# Data Pipeline for a Single Scene
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This outlines how to collect and process data for a single scene. See [here](dataset_organization.md) for how the dataset is organized.
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This outlines how to collect and process data for a single scene. See [here](dataset_organization.md) for how the dataset is organized. The steps here are split across code in two repos.
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- [spartan](https://github.com/RobotLocomotion/spartan) handles the raw data collection and tsdf fusion.
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- pdc handles change detection and rendering.
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## Capture Raw data with Kuka
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## Spartan
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### Capture Raw data with Kuka
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The quick version of raw data collection currently is:
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#### Human-moved objects
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##### Human-moved objects
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1. Start Kuka, run Position control
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2. `kip` (shortcut for Kuka Iiwa Procman) then in procman:
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2. `./capture_scene_client.py`
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3. This will create a new folder with the current date (e.g. `2018-04-07-20-23-56`) and the `raw/fusion.bag` file as in the folder structure above.
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#### Autonomous robot-moved objects
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##### Autonomous robot-moved objects
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1. Start Kuka, run Position control
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2. `kip` (shortcut for Kuka Iiwa Procman) then in procman:
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4. Run Director
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3. In Director terminal (f8), enter: `graspSupervisor.testInteractionLoop()`
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## TSDF Fusion
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### TSDF Fusion
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This is done in `spartan`. Navigate to `spartan/src/catkin_projects/fusion_server/scripts`. With `log_dir` set to the directory of your log, i.e. the full path to `2018-04-07-20-23-56` run
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```
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3. Convert the tsdf fusion to a mesh and save it as `processed/fusion_mesh.ply`
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4. Downsample the images in `processed/images` and only keep those with poses that are sufficiently different.
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## Change Detection and Depth Image Rendering
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## PDC
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### Change Detection and Depth Image Rendering
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This is done in `pytorch-dense-correspondence`. In `pdc`
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1. `use_pytorch_dense_correspondence`

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