smart-bbox 0.1.22

Official SMART pipeline for tight 3D bounding boxes

SMART: Split, Merge, and Refine

Official implementation of Split, Merge, and Refine: Fitting Tight Bounding Boxes via Over-Segmentation and Iterative Search, 3DV 2024.

Paper | arXiv | Quickstart | Pipeline | Package Docs | All Docs

Chanhyeok Park and Minhyuk Sung

SMART fits a compact set of tight 3D bounding boxes to a mesh without human supervision. The package uses a Python CLI/API with a native C++ SMART core and the fixed vendored Manifold backend.

Highlights

• Paper-style pipeline: normalize mesh, tetrahedralize, pre-segment, merge, refine, run MCTS, render, and evaluate.
• Native C++ backend for the SMART core through smart._cpp and smart-cpp-native.
• Fixed vendored Manifold source is kept unchanged for exact geometry operations.
• Python API and CLI for reproducible experiments and package use.
• ShapeNet airplane/chair/table reproduction configs are included.

Installation

Install the package:

python -m pip install "smart-bbox[pipeline]"

Install from source:

git clone https://github.com/chpark1111/SMART.git
cd SMART
python -m pip install -e ".[pipeline]"

Verify the install:

smart --config configs/smoke_5.yaml doctor
smart-cpp-native --help

For the complete install and reproduction path, see docs/QUICKSTART.md.

Documentation

Main user docs:

• Quickstart: install, verify, prepare tools/data, and run a small reproduction.
• Pipeline: stage order, config control, rendering, failure handling, and parameter overrides.
• Python Package: CLI, Python API, native executable, packaged configs, and library usage.
• Tetra Failure Playbook: why Mesh2Tet/fTetWild fails, how SMART records failures, and which repair knobs to try.
• Repository Structure: public release layout versus ignored local data, runs, external tools, and experiments.

Maintainer and research docs:

• Release Guide: local release preflight, wheel checks, tags, and PyPI publishing.
• Release Notes 0.1.0: current release scope and verification notes.
• Learned Router Release Snapshot: current packaged learned SMART+Agent path, accuracy/speed gates, and Transformer comparison.
• Learned Geometry Router: packaged DeepSets refine router, hard-state gates, exact-call reduction, and quality reinvestment experiments.
• Research Plan: RL/deep learning priors, MCTS upgrade, memory/table-based search, and promotion rules.

Local Example

If this checkout already has local ShapeNet meshes under data/, create a 3-per-category example set and run it:

bash examples/prepare_sample_shapes.sh
bash examples/run_example_3x3.sh

The example meshes are copied to examples/sample_shapes/, which is ignored by git and excluded from packages.

Data

SMART expects ShapeNet-style mesh folders:

data/shapenet_airplane/<model_id>/model.obj
data/shapenet_chair/<model_id>/model.obj
data/shapenet_table/<model_id>/model.obj

Paper category synsets:

airplane  02691156
chair     03001627
table     04379243

Prepare zipped category archives:

python scripts/prepare_shapenet_samples.py \
  --archive-dir /path/to/shapenet_zips \
  --output-root data/expanded \
  --categories airplane chair table \
  --limit 100000 \
  --normalize preserve

SMART writes normalized meshes under runs/<profile>/normalized/; it does not modify the downloaded meshes in data/.

Build External Tools

Full reproduction from raw meshes requires Mesh2Tet tools, CoACD, and the fixed Manifold runtime. In a source checkout:

smart --config configs/smoke_5.yaml build-tools

After installing from a wheel, run the same setup in a writable project/cache directory:

export SMART_TOOLS_ROOT="$PWD/.smart-tools"
smart --config smoke_5.yaml build-tools

pip install installs the SMART Python package and the bundled native SMART C++ extension/executable from wheels. It intentionally does not clone and compile Mesh2Tet/fTetWild/ManifoldPlus during installation. Those external builds are large, platform-specific, and can require local compiler/system packages, so SMART exposes them as an explicit smart build-tools step instead. That one command prepares ManifoldPlus, fTetWild, the CoACD Python CLI runtime, the fixed Manifold runtime, and the local smart._cpp/ smart-cpp-native build for a source checkout. It is idempotent: if CoACD already probes successfully, SMART skips the slow editable install; if source editable installation fails, SMART tries the PyPI CoACD runtime and only fails the command when no working coacd CLI is found.

Use smart --config configs/smoke_5.yaml build-cpp only when you need to rebuild the SMART C++ extension/executable without rebuilding external tools.

Prebuilt binaries can also be supplied:

export SMART_MANIFOLDPLUS_BIN=/path/to/ManifoldPlus/build/manifold
export SMART_FTETWILD_BIN=/path/to/fTetWild/build/FloatTetwild_bin
export SMART_COACD_BIN=/path/to/coacd
export SMART_MANIFOLD_PYTHON=/path/to/smart/vendor/manifold/build/bindings/python

Tetrahedralization Failures

Mesh2Tet can fail on noisy ShapeNet meshes because the input OBJ may be non-watertight, self-intersecting, degenerate, or split into awkward components. SMART handles this per mesh, not as a fatal dataset error:

• logs each ManifoldPlus/fTetWild attempt under runs/<profile>/logs/tetra/;
• retries with finer settings, coarser settings, --coarsen, and robust winding number settings;
• validates that tetra.msh and tetra.msh__sf.obj exist and are usable;
• records failed attempts in the tetra manifest, then skips downstream stages for that mesh while continuing the rest of the dataset.

Before tetrahedralization, SMART runs conservative mesh cleanup. The tetra stage also classifies failures and queues targeted repair retries automatically:

Detected failure	Likely cause	Automatic SMART response
surface is not watertight	holes or open mesh boundaries	retry with a temporary fill_holes=true repaired input
fTetWild/ManifoldPlus timeout or crash	self-intersection, very thin parts, degenerate faces, non-manifold edges	retry with conservative repaired input and robust/coarser parameter attempts
tetra element count below minimum	tetra parameters too fine/coarse or damaged repair output	keep fine/coarse retry schedule and record the failed parameters
ManifoldPlus repaired surface is too large	repair exploded the face count and fTetWild is likely to hit the timeout	opt-in tetra.max_manifold_faces_for_ftetwild guard skips that repaired surface before fTetWild
disconnected components	true multi-part shape or small detached fragments	only use keep_largest_component=true if explicitly enabled, because it can delete real parts

Repaired inputs are written under runs/<profile>/logs/tetra/...; SMART never mutates the original data/ OBJ. More destructive rescue, such as keep_largest_component=true, is available in config but is off by default because it can remove real disconnected shape parts. A failed mesh is therefore usually recoverable by either enabling a stronger repair variant or loosening/coarsening the tetra parameters, but SMART will not silently corrupt the shape just to force success.

See docs/TETRA_FAILURE_PLAYBOOK.md for debug commands and stronger repair options.

Run SMART

Smoke run through the Python pipeline:

smart --config configs/smoke_5.yaml run
smart --config configs/smoke_5.yaml summary

Run one mesh through the native C++ executable:

smart-cpp-native run-pipeline \
  --input data/shapenet_airplane/<model_id>/model.obj \
  --work_dir runs/native_one/<model_id> \
  --manifoldplus_bin external/mesh2tet/ManifoldPlus/build/manifold \
  --ftetwild_bin external/mesh2tet/fTetWild/build/FloatTetwild_bin \
  --coacd_bin external/CoACD/python/package/bin/coacd \
  --epsilon 0.002 \
  --edge_length 0.1 \
  --refine_max_step 2000 \
  --mcts_iter 3000

Run a native batch:

smart-cpp-native run-batch \
  --data_root data \
  --categories shapenet_airplane,shapenet_chair,shapenet_table \
  --limit_per_category 1 \
  --output_root runs/native_batch \
  --manifoldplus_bin external/mesh2tet/ManifoldPlus/build/manifold \
  --ftetwild_bin external/mesh2tet/fTetWild/build/FloatTetwild_bin \
  --coacd_bin external/CoACD/python/package/bin/coacd \
  --jobs auto \
  --reuse_existing \
  --resume_success

smart-cpp-native batch-summary \
  --manifest runs/native_batch/native_pipeline.jsonl

Evaluate And Render

Evaluate bbox outputs with the paper metrics:

smart --config configs/smoke_5.yaml evaluate

Render final boxes:

smart --config configs/smoke_5.yaml render \
  --set render.transparent=true \
  --set render.joint_mesh=false

The default renderer is the packaged software preview renderer so macOS does not launch Blender during normal runs. The adapted paper Blender renderer is still packaged under smart/legacy/renderer and can be enabled explicitly:

smart --config configs/smoke_5.yaml --set render.backend=blender render

Python API

import smart

cfg = smart.load_config("configs/smoke_5.yaml")
records = smart.run(cfg)
print(records[-1])

Package/API details are in docs/PYTHON_PACKAGE.md.

Research acceleration hooks are also exposed from smart.cpp. The packaged DeepSets refine router ranks candidate edits cheaply, then exact-scores the selected subset with native SMART/Manifold before applying an action. The accepted reward therefore remains exact; the speed gain comes from fewer exact geometry calls.

Installed wheels expose the packaged learned-router status and checkpoint:

smart learned-release-readiness --json
smart learned-release-readiness --fail-if-not-ready
smart learned-router-summary --json
smart assets --kind policies --json

From Python:

import smart

print(smart.learned_router_profile_summary()["validation_snapshot"])
print(smart.asset_path("policies", "default"))

The learned refine helper's profile="auto" now resolves to the v9 production-candidate router for multibox states while preserving exact native refine for one-box states:

import smart.cpp as sc

engine = sc.NativeSmartEngine(...)
result = sc.run_builtin_deepset_policy_refine(
    engine,
    max_steps=4,
    profile="auto",
)

For the current fallback-free MCTS/portfolio replacement research profile, use the packaged v13 DAgger policy and one of the explicit v13 profiles:

import smart.cpp as sc

engine = sc.NativeSmartEngine(...)

# Quality-safe: 2000/2000 strict replay-ready states matched the exact
# candidate portfolio, with fewer exact checks but roughly neutral wall time.
result = sc.run_builtin_deepset_policy_refine(
    engine,
    max_steps=4,
    policy="mcts_replacement_v13",
    profile="mcts_replacement_v13_quality_safe",
)

# Faster held-out profile: val/test zero-regret with stronger runtime gain,
# but not yet all-split zero-regret.
fast_result = sc.run_builtin_deepset_policy_refine(
    engine,
    max_steps=4,
    policy="mcts_replacement_v13",
    profile="mcts_replacement_v13_heldout_fast",
)

Local validation for this profile:

full token split 1015 states: 0 losses, 30.5% fewer exact calls, 1.204x vs exact oracle
1000 replay states:        0 losses, 30.7% fewer exact calls, 1.203x vs exact oracle
held-out test 264 states:  0 losses, 38.7% fewer exact calls, 1.361x vs exact oracle
target50 v11 runtime:      300 states, 0 losses, 30.5% fewer exact calls, 1.115x vs exact oracle

Large GPU Transformer teacher status:

d512/l8/e16 MPS teacher: 2298 audited candidate-set steps
guide regret -> transformer regret: 3.1165 -> 0.0121
positive top-1: 2273/2298, exact-best top-1: 2004/2298
live C++ path: native proxy/top-K remains faster than per-state PyTorch/MPS inference
C++ DeepSets runtime candidate: 300 states, 0 losses, 30.5% fewer exact calls, 1.115x wall-time speedup

The Transformer path is therefore used for teacher scoring, hard-state mining, and distillation research. The release runtime keeps C++ native routing plus exact SMART/Manifold validation.

Current learned replacement status:

mcts_replacement_v13_quality_safe:
  strict replay-ready split: 2000/2000 zero-regret against the exact candidate portfolio
  exact-call reduction:      13.6%
  wall-time effect:          roughly neutral on the local benchmark
  runtime:                   C++ DeepSets scorer + exact SMART/Manifold validator

mcts_replacement_v13_heldout_fast:
  val/test:                  zero-regret
  exact-call reduction:      26.5%
  wall-time effect:          about 1.07x over all splits
  caveat:                    4 train losses, so not the quality-safe profile

This is a learned candidate-ranking replacement for MCTS/exhaustive candidate portfolio search, not a replacement for exact geometry scoring. The stronger research goal, geometry_model_only without exact top-K selection, remains a research gate and is not the public default.

The portfolio and MCTS-prior helpers remain available for research sweeps: smart.cpp.run_builtin_deepset_portfolio_refine(...) and smart.cpp.run_builtin_deepset_prior_mcts(...). The packaged configs/learned_auto_safe.yaml profile is a quick local validation preset:

smart --config configs/learned_auto_safe.yaml run

The same router can be enabled in the normal pipeline:

smart --config configs/smoke_5.yaml refine \
  --set refine.learned_router.enabled=true \
  --set refine.learned_router.profile=auto

See docs/PYTHON_PACKAGE.md for profile details.

The variable-length macro-skill controller is a release-candidate opt-in post-refinement path. It proposes reusable multi-step fitting skills and accepts only exact SMART/Manifold non-worse updates:

Config profile:

smart --config configs/learned_macro_safe.yaml run

Stage-source validated top-3 program-gate profile:

smart --config configs/learned_macro_program_gate_top3.yaml run

This tighter opt-in profile keeps the exact SMART/Manifold validator, disables macro-memory reranking, and evaluates only the top three mined 3D macro-skill programs. The current release gate passes both post-refine and post-MCTS stage-source replay (456/456 accepted for each) while reducing exact macro-skill attempts by 81.25% versus the 16-skill portfolio.

Learned MCTS-replacement default-agent profile:

smart run
smart agent-run
smart run --agent

Equivalent explicit config:

smart --config configs/learned_default.yaml run
smart --config learned_default run

With no explicit --config, smart run now uses this learned default-agent profile. It skips MCTS, feeds refine output into the packaged macrohash skill bank, ranks a 16-skill exact portfolio with the learned JSON MLP selector, tries the learned top-3 first, and falls back to the full exact portfolio when the learned attempts are not confidently separated. The guarded candidate has passed the current 510-state MCTS-replacement gate with zero losses versus the exact fallback portfolio and 26.3% fewer exact skill attempts. The paper reproduction profiles remain available by passing an explicit config such as --config configs/paper_like.yaml.

Use smart run --category chair --mesh <mesh_id> or smart agent-run --category chair --mesh <mesh_id> to run the learned default-agent path on a subset. If --config is provided, run respects that config; agent-run additionally applies the learned agent overlay. Use smart --config <your_config.yaml> run --agent to keep your data/config but replace the MCTS stage with the guarded learned agent overlay.

Python API:

import smart

records = smart.run_agent(
    category="chair",
    meshes=["11b7c86fc42306ec7e7e25239e7b8f85"],
)

records = smart.run()
records = smart.run("configs/smoke_5.yaml", agent=True)

The underlying explicit guarded profile is also available:

smart --config configs/learned_macro_mcts_replacement_guarded.yaml run

Pipeline stage usage:

smart --config configs/smoke_5.yaml \
  --set macro_skill.input_stage=mcts \
  --set macro_skill.quality_preset=balanced \
  macro_skill

The stage first looks for regular mcts/refine stage outputs and then falls back to C++ native one-shot outputs under runs/<profile>/native_pipeline/<category>/<mesh>/{mcts,refine}_bboxs_steps0. This means smart native-pipeline results can be polished by macro_skill without manually copying bbox_params.json.

To render the macro-skill output in the same pipeline:

smart --config configs/smoke_5.yaml \
  --set stages.macro_skill=true \
  --set render.input_stage=macro_skill \
  run

If no macro skill improves the exact SMART score, the stage restores and exports the input bbox state so downstream rendering and evaluation still have a valid output.

Prepared-state CLI usage:

smart macro-skill \
  --msh runs/example/tetra/airplane/0001/tetra.msh \
  --bbox-metadata runs/example/mcts/airplane/0001/bbox_params.json \
  --category airplane \
  --quality-preset balanced \
  --output runs/example/macro_skill/airplane/0001/result.json \
  --output-bbox-dir runs/example/macro_skill/airplane/0001/bboxs

The default macro-skill executor is the compact C++ path. Use --no-native-executor only for ablations against the Python skill loop. Check the packaged release gate with:

smart learned-release-readiness --json
smart learned-release-readiness --fail-if-not-ready
smart macro-skill-summary --json

Use --quality-preset efficient to spend the higher exact budget only on the currently validated chair-like scheduler bucket. Use the learned Pareto family --quality-preset learned_fast, learned_efficient, or learned_quality to let a packaged state-conditioned ridge gate decide where to spend higher exact budget from native geometry features. Use --quality-preset quality to spend all top-k exact skill attempts for stronger quality polishing. See docs/LEARNED_ROUTER.md for the current benchmark evidence and safety contract.

Repository Layout

smart/        Python package, CLI/API, configs, pipeline wrappers
cpp/          Native C++ SMART core and smart-cpp-native executable
configs/      Source-checkout YAML profiles
examples/     Public shell examples; local sample meshes are ignored
scripts/      Supported data prep, release, and reproduction utilities
tests/        Package/native/release tests
docs/         User docs, paper assets, and release notes
experiments/  Ignored local research configs, scripts, assets, and tests
data/         Local ShapeNet data only; not packaged
runs/         Local outputs only; not packaged
external/     Downloaded Mesh2Tet/CoACD tools; not packaged
past_codes/   Original research archive; reference only

See docs/REPOSITORY_STRUCTURE.md for more detail.

Configs

Recommended public configs:

• configs/smoke_5.yaml: fast local smoke test.
• configs/example_3x3.yaml: 3 meshes per paper category from local example data.
• configs/demo.yaml: small demo profile.
• configs/paper_like.yaml: paper-style parameters.
• configs/expanded_full.yaml: larger local ShapeNet layout.

Experimental RL, pruning, and acceleration profiles are local-only under experiments/configs/; they are ignored by git and excluded from release packages.

Research directions for learned policy/value agents, MCTS priors, local-minimum escape policies, and memory/table-based search are tracked in docs/RESEARCH_PLAN.md.

Compatibility Notes

pymesh.py is a compatibility shim, not the external PyMesh package. It keeps legacy SMART code that imports pymesh working by forwarding to smart.pymesh_compat. New code should import smart.pymesh_compat directly.

Release

Build and validate release artifacts:

smart-release-preflight \
  --dist-dir /private/tmp/smart_release_check \
  --venv-dir /private/tmp/smart_release_venv \
  --recreate-venv \
  --run-asan-smoke

The preflight also checks the learned-router, macro-skill, and learned default-agent release contract with smart learned-release-readiness --fail-if-not-ready --require-default-ready, both from the source checkout and from the installed wheel.

Release notes and publishing steps are in docs/RELEASE.md.

Citation

@inproceedings{park2024smart,
  title = {Split, Merge, and Refine: Fitting Tight Bounding Boxes via Over-Segmentation and Iterative Search},
  author = {Park, Chanhyeok and Sung, Minhyuk},
  booktitle = {International Conference on 3D Vision (3DV)},
  year = {2024}
}

License

This project is released for non-commercial research under CC BY-NC-SA 4.0. See LICENSE.