MMScan devkit (#88)

* mmscan-devkit v1

* mmscan-devkit v1

* mmscan-devkit first version

* mmscan-devkit first version

* mmscan-devkit first version

* mmscan-devkit v1

* mmscan-devkit v1

* mmscan-devkit v1

* mmscan-devkit v1

* mmscan-devkit v1

* edit README.md

* mmscan-devkit

* mmscan-devkit

* mmscan-devkit

* fix some typos

* fix some typos

* fix some typos

* add picture

Author: rbler1234

.dev_scripts/covignore.cfg

@@ -4,3 +4,4 @@
 # .*/utils.py
 
 .*/__init__.py
+models/

.dev_scripts/diff_coverage_test.sh

@@ -35,7 +35,7 @@ done
 if [ ! -z "${PY_FILES}" ]
 then
     if [ "$REUSE_COVERAGE_REPORT" == "0" ]; then
-        coverage run --branch --source embodiedscan -m pytest tests/
+        coverage run --branch --source mmscan -m pytest tests/
     fi
     coverage report --fail-under 80 -m $PY_FILES
     interrogate -v --ignore-init-method --ignore-module --ignore-nested-functions --ignore-magic --ignore-regex "__repr__" --fail-under 95 $PY_FILES

.dev_scripts/linter.sh

@@ -1,3 +1,3 @@
-yapf -r -i embodiedscan/ configs/ tests/ tools/
-isort embodiedscan/ configs/ tests/ tools/
+yapf -r -i mmscan/ data_preparation/
+isort mmscan/ data_preparation/
 flake8 .

.github/workflows/lint.yml

@@ -24,4 +24,4 @@ jobs:
       - name: Check docstring coverage
         run: |
           pip install interrogate
-          interrogate -v --ignore-init-method --ignore-magic --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 90 embodiedscan
+          interrogate -v --ignore-init-method --ignore-magic --ignore-module --ignore-nested-functions --ignore-regex "__repr__" --fail-under 90 mmscan

.github/workflows/merge_stage_test.yml

@@ -38,11 +38,11 @@ jobs:
           apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
       - name: Install system dependencies
         run: apt-get update && apt-get install -y git ffmpeg libturbojpeg
-      - name: Install dependencies and EmbodiedScan
+      - name: Install dependencies and MMScan
         run: python install.py all
       - name: Run unittests and generate coverage report
         run: |
-          coverage run --branch --source embodiedscan -m pytest tests
+          coverage run --branch --source mmscan -m pytest tests
           coverage xml
           coverage report -m
 
@@ -67,10 +67,10 @@ jobs:
           apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
       - name: Install system dependencies
         run: apt-get update && apt-get install -y git ffmpeg libturbojpeg
-      - name: Install dependencies and EmbodiedScan
+      - name: Install dependencies and MMScan
         run: python install.py all
       - name: Run unittests and generate coverage report
         run: |
-          coverage run --branch --source embodiedscan -m pytest tests
+          coverage run --branch --source mmscan -m pytest tests
           coverage xml
           coverage report -m

.github/workflows/pr_stage_test.yml

@@ -34,10 +34,10 @@ jobs:
           apt-key adv --fetch-keys https://developer.download.nvidia.com/compute/machine-learning/repos/ubuntu1804/x86_64/7fa2af80.pub
       - name: Install system dependencies
         run: apt-get update && apt-get install -y ffmpeg libsm6 libxext6 git ninja-build libglib2.0-0 libsm6 libxrender-dev libxext6
-      - name: Install dependencies and EmbodiedScan
+      - name: Install dependencies and MMScan
         run: python install.py all
       - name: Run unittests and generate coverage report
         run: |
-          coverage run --branch --source embodiedscan -m pytest tests/
+          coverage run --branch --source mmscan -m pytest tests/
           coverage xml
           coverage report -m

.github/workflows/test_mim.yml

@@ -41,4 +41,4 @@ jobs:
       - name: Build and install
         run: rm -rf .eggs && mim install -e .
       - name: test commands of mim
-        run: mim search embodiedscan
+        run: mim search mmscan

.gitignore

@@ -14,8 +14,7 @@ dist/
 downloads/
 eggs/
 .eggs/
-lib/
-lib64/
+
 parts/
 sdist/
 var/
@@ -113,7 +112,6 @@ venv.bak/
 
 # demo
 *.jpg
-*.png
 *.obj
 *.ply
 demo/data/*
@@ -127,7 +125,7 @@ data/3rscan
 data/matterport3d
 data/arkitscenes
 data/*.pkl
-data/*.json
+
 exps/
 todo.md
 
@@ -140,4 +138,3 @@ tools/*.sh
 
 # test submission results
 *.pkl
-*.json

.pre-commit-config.yaml

@@ -3,6 +3,13 @@ repos:
     rev: 5.0.4
     hooks:
       - id: flake8
+        exclude: '^models/.*'
+  # - repo: https://github.com/ambv/black
+  #   rev: 23.9.1
+  #   hooks:
+  #     - id: black
+  #       args: [--line-length=79]
+  #       exclude: '^models/.*'
   - repo: https://github.com/PyCQA/isort
     rev: 5.11.5
     hooks:

README.md

@@ -0,0 +1,51 @@
+### Prepare MMscan info files.
+
+Given the licenses of respective raw datasets, we recommend users download the raw data from their official websites and then organize them following the below guide.
+Detailed steps are shown as follows.
+
+1. Download ScanNet v2 data [HERE](https://github.com/ScanNet/ScanNet). Link or move the folder to this level of directory.
+
+2. Download 3RScan data [HERE](https://github.com/WaldJohannaU/3RScan). Link or move the folder to this level of directory.
+
+3. Download Matterport3D data [HERE](https://github.com/niessner/Matterport). Link or move the folder to this level of directory.
+
+4. Organize the file structure. You are recommanded to create a soft link to the raw data folder under `mmscan_data/embodiedscan_split/data`.
+
+   ```
+   mmscan_data/embodiedscan_split/data/
+   ├── scannet/
+   │   ├── scans
+   │   │   ├── <scene_id>
+   │   │   ├── ...
+   ├── 3rscan/
+   │   ├── <scene_id>
+   │   ├── ...
+   ├── matterport3d/
+   │   ├── <scene_id>
+   │   ├── ...
+   ```
+
+   Additionally, create a `process_pcd` folder under `mmscan_data/embodiedscan_split` to store the results. Similarly, we recommend using a symbolic link, as the total file size might be a little large (approximately 21GB)
+
+   PS: If you have followed the embodiedscan tutorial to organize the data, you can skip these steps and link or copy the `data` folder to
+   `mmscan_data/embodiedscan_split`.
+
+   After all the raw data is organized, the directory structure should be as below:
+
+   ```
+   mmscan_data
+   ├── embodiedscan_split/
+   │   ├── data/
+   │   ├── process_pcd/
+   │   ├── embodiedscan-v1/
+   │   ├── embodiedscan-v2/
+   ├── MMScan-beta-release
+
+   ```
+
+5. Read raw files and generate processed point cloud files, by running the following scripts.
+
+   ```bash
+   python process_all_scan.py --nproc 8
+   # If your various file directories do not match the configuration settings, define them using --
+   ```

assets/2024_NeurIPS_MMScan_Camera_Ready.pdf

@@ -0,0 +1 @@
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