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README.md

ScanNet Data Server

The ScanNet server is broken down into the following components:

  1. Upload process used by the iPad to upload the scan and trigger scan processing. To ensure that scans can be automatically processed, the scans should be placed in a directory with lots of space and accessible to the scanning processor. Tested on Ubuntu 14.04 Linux machine.
  2. Scan processing scripts. Require a Windows machine with high-end GPU (tested on Windows 10 with GTX Titan X with 12GB of VRAM).
  3. Indexing scripts. Go through scan folders and collate information about the scans.

Upload scripts

The upload script receives scan files (.h264, .depth, .imu, .txt, .camera) from the iPad and stores them in a staging area for scan processing. The files are first placed in the tmp directory before being moved into the staging directory. Uses flask with gunicorn with 10 worker threads on port 8000.

  • install_deps.sh - Run this to install python dependencies required for the upload server
  • start_upload_server.sh - Run this to start the upload server. The main entry point is at upload.py (started automatically by this .sh script)
  • wsgi.py - Web service wrapper for upload.py

Scan processing scripts

The scan processing requires a Windows machine with high-end GPU (tested on Titan X 12GB). It also relies on these third party tools:

TODO: Also have a brief list of our binaries that are needed and pointers to their repository locations

  • start_process_server.py - Starts the scan processing server. The main entry point is at process.py. Running this starts the upload server on port 5000, simple flask server that only handles one request at a time (will block until scan is processed).
  • scan_processor.py - Main scan processing script. Edit to see/change path for tools/applications.
  • mts_render.py - Script to render preview images of scans (ply -> png). Depends on a Mitsuba renderer installation.
  • thumbnail.sh - Script for generating thumbnails from rendered views

Indexing scripts

Indexing scripts are used to collate information about the scans and index them.

  • monitor.py - Web service entry point for monitoring and triggering of indexing of scans. Run python monitor.py to start the monitor server on port 5001 (simple flask server).
  • index.py - Creates index of scans in a directory and outputs a csv file
  • scripts/index_scannet.sh - Index both staging and checked scans and updates WebUI db

Statistics computation scripts

  • compute_annotation_stats.py - Compute aggregated annotation statistics
  • compute_timings.py - Compute processing times for scans
  • scripts/combine_stats.py - Combines statistics with index

Configuration files

  • scannet.json - Metadata for ScanNet assets for use with the Scene Toolkit viewer
  • scan_stages.json - The stages of the scan pipeline (so we can track progress)
  • upload.ini - Configuration file for upload server