AccessPath

AccessPath is an anonymous-review release for accessibility-scene amodal completion. It includes the full, finite GPU execution path from mask proposal through quality-gated 2D completion and visual 3D completion. The package also keeps two direct, mask-driven entry points:

• 2D completion: GPU inpainting that only changes the reviewed occluded region and preserves source pixels elsewhere.
• Visual 3D completion: a wrapper around the separately released Amodal3R backend that produces Gaussian-splat and dense-mesh visual reconstructions from an RGB image and an aligned three-value amodal mask.

The five supported accessibility categories are stairs, ramp, curb_cut, tactile_paving, and walkway.

Relationship to prior work

AccessPath is inspired by and adapted from established research on promptable segmentation, amodal completion, diffusion inpainting, monocular geometry, and visual 3D reconstruction. It is not presented as a reimplementation or a copy of any one prior system. The project-level contribution is the accessibility adaptation: category-specific target/obstacle prompts, a visible-hidden-amodal mask schema, constrained mask relations, mask-restricted 2D completion, geometry/quality gates, a finite Slurm workflow, and review-oriented outputs.

When AccessPath actually calls an external model, that model remains an independent backend with its own code, weights, and license. The precise boundary between inspiration, optional comparison, and runtime use is listed in docs/DEPENDENCIES_AND_WEIGHTS.md.

What is included

This repository contains project-level pipeline, mask, 2D, depth/geometry, visual-3D adapter, verification, rendering, review-bundle, Slurm, and prompt configuration code. The complete process is documented in docs/REPRODUCIBLE_PIPELINE.md, and the stage-to-source mapping is in docs/SOURCE_MANIFEST.md. For a beginner-oriented installation guide, exact dependency status, official model links, weight-download commands, and citation information, read docs/DEPENDENCIES_AND_WEIGHTS.md before running an experiment.

It deliberately contains no source images, masks, model checkpoints, environments, experiment outputs, logs, author information, or machine-specific paths.

The 100-image reviewer subset is not distributed yet. Its source-image and derived-mask redistribution status requires source-specific license and privacy clearance. A data card and a release manifest will be added only after that review is complete.

Setup

Use Linux, Python 3.10+, a CUDA-capable GPU, and an environment compatible with the chosen models. Create a fresh environment, install PyTorch for the local CUDA version, then install the lightweight utilities:

python -m pip install -r requirements/runtime.txt
python -m pip install diffusers transformers accelerate safetensors

The visual-3D backend additionally needs the official Amodal3R environment and its CUDA rasterizer dependencies. Follow the upstream installation guide; do not copy the upstream source tree or checkpoints into this repository.

Obtain models separately

No weights are redistributed here. Download each dependency only after accepting its own license and access conditions.

Component	Official source	Used by
Stable Diffusion inpainting	sd-legacy/stable-diffusion-inpainting	2D completion
Amodal3R	Sm0kyWu/Amodal3R	visual 3D completion
SAM 3 (optional mask proposal stage)	facebook/sam3	mask proposals only

Project-trained checkpoints are intentionally withheld during anonymous review. They should be released only after verifying the training-data permissions, base-model terms, privacy risk, and the paper's release policy.

The optional VGGT path and the non-runtime related-work references (pix2gestalt, Open-World AMODAL, and Amodal Completion in the Wild) are identified explicitly in docs/DEPENDENCIES_AND_WEIGHTS.md. They are not silently downloaded or executed by the default pipeline.

Input masks

All masks must match the input RGB resolution. For 2D completion, provide the visible-target, amodal-target, and obstacle masks. For visual 3D completion, provide one aligned PNG with exactly these values:

255  background
188  visible target
0    hidden target

The intended relations are hidden = amodal AND NOT visible, visible and obstacle are disjoint, and hidden is a subset of obstacle. Automatic masks are proposals and should be reviewed before they drive a completion result.

Run 2D completion

python accesspath.py 2d -- \
  --image path/to/image.jpg \
  --target-visible-mask path/to/target_visible.png \
  --target-amodal-mask path/to/target_amodal.png \
  --obstacle-mask path/to/obstacle.png \
  --category stairs \
  --model path/to/stable-diffusion-inpainting \
  --output-dir outputs/example_2d \
  --device cuda

The selected RGB result and its quality/provenance metadata are written under the output directory. Run python accesspath.py 2d -- --help for all options.

Run the complete pipeline

The complete pipeline uses a bounded Slurm GPU job, so it queues for a GPU, persists its result directory, and exits once the requested image is complete:

python accesspath.py pipeline -- \
  --image path/to/image.jpg \
  --category stairs \
  --output-dir outputs/stairs_demo

It runs mask proposals (or validates supplied reviewed masks), constrained amodal-mask inference, 2D completion, depth/geometry diagnostics, visual 3D, verification, and a compact review bundle. See docs/REPRODUCIBLE_PIPELINE.md for environment variables, stage-by-stage behavior, outputs, and optional stage switches.

Run visual 3D completion

Install the official Amodal3R runtime first, then run:

python accesspath.py 3d -- \
  --image path/to/image.jpg \
  --mask path/to/amodal_3value.png \
  --model Sm0kyWu/Amodal3R \
  --output-dir outputs/example_3d

This is a learned visual reconstruction, not metric geometry, a navigation safety label, or a guarantee of a watertight or scaled mesh. Run python accesspath.py 3d -- --help for output and rendering options.

Attribution and release boundary

The 3D adapter calls Amodal3R and does not claim to reimplement that upstream model. Consult THIRD_PARTY_NOTICES.md and the model cards for all dependency terms. Repository code is provided for anonymous academic review; a final license and any project checkpoint release will be announced with the archival paper release.