diff --git a/.github/workflows/static.yml b/.github/workflows/static.yml new file mode 100644 index 0000000..e756184 --- /dev/null +++ b/.github/workflows/static.yml @@ -0,0 +1,43 @@ +# Simple workflow for deploying static content to GitHub Pages +name: Deploy static content to Pages + +on: + # Runs on pushes targeting the default branch + push: + branches: ["project_page"] + + # Allows you to run this workflow manually from the Actions tab + workflow_dispatch: + +# Sets permissions of the GITHUB_TOKEN to allow deployment to GitHub Pages +permissions: + contents: read + pages: write + id-token: write + +# Allow only one concurrent deployment, skipping runs queued between the run in-progress and latest queued. +# However, do NOT cancel in-progress runs as we want to allow these production deployments to complete. +concurrency: + group: "pages" + cancel-in-progress: false + +jobs: + # Single deploy job since we're just deploying + deploy: + environment: + name: github-pages + url: ${{ steps.deployment.outputs.page_url }} + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v3 + - name: Setup Pages + uses: actions/configure-pages@v3 + - name: Upload artifact + uses: actions/upload-pages-artifact@v2 + with: + # Upload entire repository + path: '.' + - name: Deploy to GitHub Pages + id: deployment + uses: actions/deploy-pages@v2 diff --git a/.gitignore b/.gitignore new file mode 100644 index 0000000..db6e633 --- /dev/null +++ b/.gitignore @@ -0,0 +1,26 @@ +# Python intermediate files +*.pyc +*.pyo +*.egg-info/ +__pycache__/ +*.pyd + +# Virtual environment +venv/ +*.venv +.env + +# Jupyter notebook checkpoints +.ipynb_checkpoints/ + + + +# IDEs +# PyCharm +.idea/ + +# VS Code +.vscode/ + +# MacOS system files +.DS_Store diff --git a/imgs/arch.png b/imgs/arch.png new file mode 100644 index 0000000..769ac7a Binary files /dev/null and b/imgs/arch.png differ diff --git a/imgs/full hd/psc_qual.mp4 b/imgs/full hd/psc_qual.mp4 new file mode 100644 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+ + + + + ++ We propose the task of Panoptic Scene Completion (PSC) which extends the recently popular Semantic Scene Completion (SSC) task with instance-level information to produce a richer understanding of the 3D scene. + Our PSC proposal utilizes a hybrid mask-based technique on the non-empty voxels from sparse multi-scale completions. + Whereas the SSC literature overlooks uncertainty which is critical for robotics applications, we instead propose an efficient ensembling to estimate both voxel-wise and instance-wise uncertainties along PSC. + This is achieved by building on a multi-input multi-output (MIMO) strategy, while improving performance and yielding better uncertainty for little additional compute. Additionally, we introduce a technique to aggregate permutation-invariant mask predictions. + Our experiments demonstrate that our method surpasses all baselines in both Panoptic Scene Completion and uncertainty estimation on three large-scale autonomous driving datasets. +
++@InProceedings{cao2023pasco, + author = {Cao, Anh-Quan and Dai, Angela and de Charette, Raoul}, + title = {PaSCo: Urban 3D Panoptic Scene Completion with Uncertainty Awareness}, + booktitle={arXiv}, + year={2023}, +} ++ +