code/README_REPRODUCE.md

Reproducibility Guide

This guide maps the code release to the experiments in the paper. The primary reproducibility path is Blender indoor; other sources are retained as secondary adapters for robustness checks.

Primary Asset Requirement

Source	Expected Input	Primary Command
Blender indoor	.blend scenes	scripts/run_blender_indoor.sh

The repository does not redistribute scene assets. Reviewers can run the no-data smoke test immediately, and can run the full path after mounting local .blend scenes.

Secondary Assets

Source	Expected Input	Config
Blender outdoor / generic meshes	.glb or .gltf	configs/blender_outdoor.yaml
HM3D	.glb or .gltf plus optional semantic/navmesh files	configs/hm3d.yaml
ScanNet++	.ply	configs/scannetpp.yaml

Building Blocks Available in This Release

Module	Purpose	Entry Point
Candidate generation	Phase 1 of §3 — produce (\mathcal{P}_\varphi)	scripts/build_candidates.py
Conflict-aware selection	Phase 2 of §3 — greedy with (s_t = G_t - \lambda L_t + \beta B_t)	scripts/select_views.py
Selected-view rendering	Phase 3 — final ERP render from chosen candidates	scripts/render_selected.py
Coverage metric	§6.1 high-resolution oracle coverage	scripts/evaluate_coverage.py
Oracle-gain validation	§6.2 warping vs. pre-render-all comparison	scripts/evaluate_oracle_gap.py
Quality audit	Appendix F.2 50-frame audit	scripts/audit_quality.py
Run summarization	Aggregate per-scene selected_frames.json into a CSV	scripts/summarize_blender_indoor_run.py
Audit summarization	Aggregate per-frame audit results into a CSV	scripts/summarize_quality_audit.py

The §6 driver scripts that orchestrate these building blocks across an entire baseline sweep (e.g., the K\!\in\!\{8,16,24,32\} table of §6.1, the (\lambda) sweep of §6.5, and the four-source benchmark of §6.6) are scheduled to be released alongside the camera-ready paper.

Minimal Review Run

bash scripts/run_tiny.sh

This validates the Blender-indoor-style metadata format, greedy selection loop, render-output contract, coverage metric, oracle-gap script, and quality audit script — end-to-end without any private scene assets.

Blender-Indoor Full Run

DRY_RUN=1 \
BLENDER=/path/to/blender \
INPUT_DIR=/path/to/blend_scenes \
OUTPUT_ROOT=outputs/blender_indoor \
bash scripts/run_blender_indoor.sh

After confirming the detected scene list, remove DRY_RUN=1:

BLENDER=/path/to/blender \
INPUT_DIR=/path/to/blend_scenes \
OUTPUT_ROOT=outputs/blender_indoor \
NUM_FRAMES=30 \
RESOLUTION=2048,1024 \
GRID_SPACING=0.5 \
bash scripts/run_blender_indoor.sh

Metric Scripts

The native Blender-indoor pipeline emits selected_frames.json under each scene output directory. Summarize a completed run with:

python3 scripts/summarize_blender_indoor_run.py \
  --output-root outputs/blender_indoor \
  --output outputs/blender_indoor/results/coverage_main.csv

If you have consolidated candidate and selection metadata into the normalized JSONL/JSON contract used by the smoke test, use:

python3 scripts/evaluate_coverage.py \
  --candidates outputs/blender_indoor/metadata/candidates.jsonl \
  --selected outputs/blender_indoor/metadata/selected_viewpoints.json \
  --output outputs/blender_indoor/results/coverage_main.csv

python3 scripts/evaluate_oracle_gap.py \
  --candidates outputs/blender_indoor/metadata/candidates.jsonl \
  --selected outputs/blender_indoor/metadata/selected_viewpoints.json \
  --output outputs/blender_indoor/results/oracle_validation.csv

python3 scripts/audit_quality.py \
  --render-dir outputs/blender_indoor/renders \
  --metadata outputs/blender_indoor/metadata/selected_viewpoints.json \
  --output outputs/blender_indoor/results/audit_50_frames.csv

The exact dataset paths should be adapted to the local machine. Do not commit generated data, logs, checkpoints, third-party repositories, or scene assets to the anonymous code repository.