- Dockerfile: build the full PanDORA env on CUDA 11.7 (torch 2.0.1+cu117,
pinned via a global PIP_CONSTRAINT so nothing upgrades torch to a
driver-incompatible CUDA build), tiny-cuda-nn v1.6, pyequilib 0.5.6,
numpy<2, viser 0.0.16, and Blender. Removes the unused COLMAP/SfM stack,
caps compile parallelism (MAKE_JOBS), and sets PYTHONPATH for lantern_scripts.
- docker/build_and_run.sh: build the image (tunable CUDA_VERSION/MAKE_JOBS)
and run the pipeline or an interactive GPU shell, mounting the repo + data.
- docker/run_pandora_pipeline.sh: fixed two-stage PanDORA training pipeline.
- docker/download_scenes.sh: download PanDORA dataset scenes by name.
- README.md: rewrite as a simple 4-step Docker quick start; point advanced
users to README-pandora.md / README-hdrnerfacto.md.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
- align_two_folders.py: model-free contour+homography alignment of two
image folders, with diagnostics (checkerboards, thresholds, matched
contours, SIFT match panels).
- align_fast_to_well.py: expose the pre-RANSAC SIFT correspondences so
the new visualizations can consume them.
- ldr_res_jpg.py: LDR (jpg) variant of ldr_res.py metric computation.
- run_metrics_all.sh: run calculate_metrics_fast across all scenes.
- collect_metrics_sheet.py: gather per-scene result JSONs into a CSV.
- README.md: merge README-pandora.md and README-hdrnerfacto.md into one
guide (PanDORA + HDR-Nerfacto paths).
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
Rename the method registration and its description/config key, plus the
ns-train invocations in the Pandora README. Existing on-disk checkpoint
paths (outputs/.../lantern-nerfacto/...) are intentionally left unchanged.
Co-Authored-By: Claude Opus 4.8 <noreply@anthropic.com>
MrKarimiDandClaude Opus 4.8
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