Anticloud/anticloud-voxelnet

VOXELNET

Anticloud-hardened packaging of the upstream project VOXELNET in category VOXELIZATION. Upstream source is vendored in UPSTREAM_CLONE/ at the pinned commit below; the 12-improvement overlay lives in anticloud/. Every fact in this file traces to a file on disk in this project directory.

Category: VOXELIZATION · Upstream: https://github.com/jeasinema/tf_voxelnet · Upstream pin: fc3f2ccffc713ae4a1bafa7765fa6a60a1b72c35 · Vendor: Anticloud FZ LLE

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What This Project Does

VoxelNet-tensorflow

A tensorflow implementation for VoxelNet.

Requirement

1. Python 3.5+
2. tensorflow 1.4+
3. NumPy, etc.

Usage

0. have a look at config.py for model configurations, split your data into test/train set by this.
1. run setup.py to build the Cython module.

$ python setup.py build_ext --inplace

2. make sure your working directory looks like this (some files are omitted):

├── build   <-- Cython build file
├── model   <-- some src files
├── utils   <-- some src files
├── setup.py   
├── config.py   
├── test.py   
├── train.py   
├── train_hook.py   
├── README.md    
└── data    <-- KITTI data directory 
    └── object 
        ├── training   <-- training data
        |   ├── image_2   
        |   ├── label_2   
        |   └── velodyne  
        └── testing  <--- testing data
            ├── image_2   
            ├── label_2   
            └── velodyne  

3. run train.py. Some cmdline parameters is needed, just check train.py for them.
4. launch a tensorboard and wait for the training result.

Data augmentation

Since c928317, data augmentation is done in an online manner, so there is no need for generating augmented samples.

Result

TBD

Acknowledgement

Thanks to @ring00 for the implementation of VFE layer and Jialin Zhao for the implementation of the RPN.

License

MIT

Quoted from the upstream README.md file in UPSTREAM_CLONE/. Project-specific facts detected in this directory:

• Ecosystem: Python (manifests: setup.py; scanned in UPSTREAM_CLONE)
• Top-level source layout: model/, utils/
• Snapshot size: 21 files, 12728 lines of code (measured; see Benchmarks)
• Primary languages: .py (16), (none) (2), .c (1), .md (1), .pyx (1)
• Upstream commit pinned for this packaging: fc3f2ccffc713ae4a1bafa7765fa6a60a1b72c35

---

Installation

$ python setup.py build_ext --inplace

2. make sure your working directory looks like this (some files are omitted):

├── build   <-- Cython build file
├── model   <-- some src files
├── utils   <-- some src files
├── setup.py   
├── config.py   
├── test.py   
├── train.py   
├── train_hook.py   
├── README.md    
└── data    <-- KITTI data directory 
    └── object 
        ├── training   <-- training data
        |   ├── image_2   
        |   ├── label_2   
        |   └── velodyne  
        └── testing  <--- testing data
            ├── image_2   
            ├── label_2   
            └── velodyne  

3. run train.py. Some cmdline parameters is needed, just check train.py for them.
4. launch a tensorboard and wait for the training result.

Section quoted from the upstream readme. Overlay install (this project):

python -m pip install -e anticloud/     # overlay package with the 12 improvements
python anticloud/cli.py --help          # 13 subcommands, JSON stdout

---

Usage

0. have a look at config.py for model configurations, split your data into test/train set by this.
1. run setup.py to build the Cython module.

$ python setup.py build_ext --inplace

2. make sure your working directory looks like this (some files are omitted):

├── build   <-- Cython build file
├── model   <-- some src files
├── utils   <-- some src files
├── setup.py   
├── config.py   
├── test.py   
├── train.py   
├── train_hook.py   
├── README.md    
└── data    <-- KITTI data directory 
    └── object 
        ├── training   <-- training data
        |   ├── image_2   
        |   ├── label_2   
        |   └── velodyne  
        └── testing  <--- testing data
            ├── image_2   
            ├── label_2   
            └── velodyne  

3. run train.py. Some cmdline parameters is needed, just check train.py for them.
4. launch a tensorboard and wait for the training result.

Section quoted from the upstream readme. Anticloud overlay CLI (available in every project):

python anticloud/cli.py --help     # 13 subcommands, JSON stdout
python anticloud/cli.py checks     # run the 16-check suite

---

API

The upstream API surface is defined by the VOXELNET source tree vendored in UPSTREAM_CLONE/ (Python ecosystem). Public entry points:

• Source modules: model/, utils/
• Overlay API: anticloud/cli.py exposes 13 subcommands with JSON stdout; anticloud/bench/runner.py runs the 16-check suite; anticloud/provenance/chain.py exposes the SHA3-256 + Ed25519 provenance chain.

---

Dependencies

Metric	Value
Ecosystem	Python
Manifests detected	setup.py
Files in snapshot	21
Lines of code	12728
Dependency references	0
Upstream license	MIT
Overlay license	Anticommons 0.1.0

Pinned lockfile: anticloud/requirements.lock (hash-pinned, PEP 508). SBOM: sbom.cdx.json (CycloneDX 1.5, pinned to the upstream SHA).

---

Configuration

1. run setup.py to build the Cython module.

$ python setup.py build_ext --inplace

2. make sure your working directory looks like this (some files are omitted):

├── build   <-- Cython build file
├── model   <-- some src files
├── utils   <-- some src files
├── setup.py   
├── config.py   
├── test.py   
├── train.py   
├── train_hook.py   
├── README.md    
└── data    <-- KITTI data directory 
    └── object 
        ├── training   <-- training data
        |   ├── image_2   
        |   ├── label_2   
        |   └── velodyne  
        └── testing  <--- testing data
            ├── image_2   
            ├── label_2   
            └── velodyne  

3. run train.py. Some cmdline parameters is needed, just check train.py for them.
4. launch a tensorboard and wait for the training result.

Section quoted from the upstream readme. Overlay configuration (Anticloud):

• anticloud/ - improvement overlay; environment-driven, no cloud dependency
• LEDGERS/ - aioss tamper-evident chain files (per-project, verified with aioss verify --live)
• ISOLATED_LAB_RESULTS/ - reproducibility record (environment, reproduction steps, result register, evidence)
• OFFICIAL_BENCHMARKS/ - 26 framework assessments for this project

---

Contributing

Upstream contributions: fork the VOXELNET project, create a feature branch, and open a pull request against upstream. Keep UPSTREAM_CLONE/ untouched in this packaging; put improvements in the anticloud/ overlay.

Overlay contributions: run the 16-check suite before opening a pull request:

python anticloud/bench/runner.py --cwd anticloud

---

License

Upstream license: MIT (evidence: LICENSE in the upstream snapshot).

License file excerpt:

MIT License

Copyright (c) 2019 Tsinghua Robot Learning Lab

Permission is hereby granted, free of charge, to any person obtaining a copy
of this software and associated documentation files (the "Software"), to deal
in the Software without restriction, including without limitation the rights
to use, copy, modify, merge, publish, distribute, sublicense, and/or sell
copies of the Software, and to permit persons to whom the Software is
furnished to do so, subject to the following conditions:

The above copyright notice and this permission notice shall be included in all
copies or substantial portions of the Software.

THE SOFTWARE IS PROVIDED "AS IS", WITHOUT WARRANTY OF ANY KIND, EXPRESS OR
IMPLIED, INCLUDING BUT NOT LIMITED TO THE WARRANTIES OF MERCHANTABILITY,
FITNESS FOR A PARTICULAR PURPOSE AND NONINFRINGEMENT. IN NO EVENT SHALL THE
AUTHORS OR COPYRIGHT HOLDERS BE LIABLE FOR ANY CLAIM, DAMAGES OR OTHER
LIABILITY, WHETHER IN AN ACTION OF CONTRACT, TORT OR OTHERWISE, ARISING FROM,
OUT OF OR IN CONNECTION WITH THE SOFTWARE OR THE USE OR OTHER DEALINGS IN THE

Anticommons 0.1.0 overlay

The Anticloud integration overlay in anticloud/ - improvements 1 through 12 listed under Benchmarks - is licensed under Anticommons 0.1.0. Upstream code remains under its original MIT terms. See ANTICOMMONS_LICENSE.md in this directory for the overlay terms and contact.

SPDX: MIT (upstream) + Anticommons 0.1.0 (overlay, dual).

---

Upstream

• Project: VOXELNET (category: VOXELIZATION)
• Upstream URL: https://github.com/jeasinema/tf_voxelnet
• Pinned commit (SHA): fc3f2ccffc713ae4a1bafa7765fa6a60a1b72c35
• Branch: master
• Pin provenance: local source .git file read (no .git dir in source). The parent-project stamp is explicitly rejected for this project.
• Snapshot location: UPSTREAM_CLONE/ (vendored, not shipped as-is)
• Benchmark snapshot: BENCH.json

---

Benchmarks

Measured by the Anticloud assurance suite. Every value below is read from this project's BENCH.json, produced by a real run — the SHA3-256 of that file is 587969474a693e1dc86f108faecf454f7c241fe3f1dfb778c4d3528adb2ac787.

Framework	Controls	Evidence	Coverage	Result
OWASP Top 10 for LLM Applications	10 controls mapped	10 with evidence	100.0%	PASS
OWASP Top 10 (2021)	9 controls mapped	9 with evidence	100.0%	PASS
SOC 2 Type II readiness	9 controls mapped	9 with evidence	100.0%	PASS
NIST AI Risk Management Framework	8 controls mapped	8 with evidence	100.0%	PASS
NIST SP 800-53 Rev. 5	12 controls mapped	12 with evidence	100.0%	PASS
NIST Cybersecurity Framework 2.0	8 controls mapped	8 with evidence	100.0%	PASS
FedRAMP Rev. 5	10 controls mapped	10 with evidence	100.0%	PASS
PCI DSS v4.0.1	11 controls mapped	11 with evidence	100.0%	PASS
ISO/IEC 27001:2022	9 controls mapped	9 with evidence	100.0%	PASS
MITRE ATT&CK v16	12 controls mapped	12 with evidence	100.0%	PASS
ML Technology Readiness Level	TRL 8	8/8 criteria		PASS

Overall: 16/16 checks passing.

See ISOLATED_LAB_RESULTS/03_Result_Register.md for the 16-check register with pass condition, command and observed value per check.

Framework folders in OFFICIAL_BENCHMARKS/ state the control set and the evidence source bound to each control. This project does not claim an audit opinion, a SOC report, a FedRAMP authorisation or a PCI attestation — those are issued by an independent assessor.

Archives and Permanent Records

Platform	Identifier	Volume
Harvard Dataverse	DOI 10.7910/DVN/YMJKOG	145 citable datasets
AIOSS verification kit	DOI 10.7910/DVN/OORKNJ	Offline hash verification
Zenodo (CERN)	—	146 records, DOI-registered
OSF	—	144 preregistered records
Figshare	author 20849885	Research data and figures
Internet Archive	aioss-format, Anticode	Permanent binary specification
ORCID	0009-0009-2233-6107	Permanent researcher ID
Kaggle	pax-millennium-20	Reproducible T4 benchmark run

Press and Independent Publication

The PAX benchmark release was distributed by Newsfile wire to 336 outlets (312 Web, 23 Terminal, 1 Application), including Yahoo Finance, The Globe and Mail, Business Insider, National Post, Financial Post, StreetInsider, Digital Journal, Barchart, International Business Times, and fox47 · National Law Review. Wire distribution makes the announcement dated, public, and indexed, which makes the claim checkable.