Deepcompress

C+ 72 completed
Ai Ml
library / python · small
71
Files
10,774
LOC
1
Frameworks
6
Languages

Pipeline State

completed
Run ID
#370794
Phase
done
Progress
1%
Started
Finished
2026-04-13 01:31:02
LLM tokens
0

Pipeline Metadata

Stage
Cataloged
Decision
proceed
Novelty
61.33
Framework unique
Isolation
Last stage change
2026-05-10 03:35:28
Deduplication group #50476
Member of a group with 10 similar repo(s) — canonical #17208 view group →
Top concepts (2)
Project DescriptionTesting
Repobility · code-quality intelligence platform · https://repobility.com

AI Prompt

Create a Python library called DeepCompress for efficient point cloud geometry compression. The tool should handle the encoding and decoding process using deep learning principles. I need to ensure the structure supports testing, so please set up the necessary pytest configurations. The library should be designed for applications like self-driving cars, AR/VR, and medical imaging, and I want to incorporate advanced entropy models like 'gaussian' and 'hyperprior' for compression.
python library point-cloud compression deep-learning pytest geometry lidar
Generated by gemma4:latest

Catalog Information

DeepCompress is an efficient point cloud geometry compression tool designed for various applications, including self-driving cars, virtual/augmented reality, and medical imaging.

Description

DeepCompress uses deep learning to compress point clouds by analyzing their patterns and structure. It encodes the point cloud into a compact summary, compresses it using entropy coding, decompresses the summary, and decodes the original point cloud. This results in 10-100x smaller files with minimal quality loss.

الوصف

يستخدم DeepCompress التعلم العميق لضغط النقاط المتراكمة عن طريق تحليل مظاهرها والتركيب. يتحول النقاط إلى تلخيص ملحوظ، ويضغط باستخدام الترميز بالانتروبي، ويتم استرجاع الملخص، ويعاد ترميز النقاط الأصلية. هذا يؤدي إلى ضغط 10-100 مرة أقل مع فقدان جودة قليل.

Novelty

9/10

Tags

point-cloud-compression deep-learning entropy-coding 3d-modeling self-driving-cars virtual-reality medical-imaging

Technologies

keras matplotlib numpy pandas scipy tensorflow

Claude Models

claude-opus-4.6

Quality Score

C+
72.2/100
Structure
88
Code Quality
54
Documentation
63
Testing
85
Practices
64
Security
92
Dependencies
60

Strengths

  • CI/CD pipeline configured (github_actions)
  • Good test coverage (100% test-to-source ratio)
  • Code linting configured (ruff (possible))
  • Consistent naming conventions (snake_case)
  • Good security practices \u2014 no major issues detected
  • Properly licensed project

Weaknesses

  • 741 duplicate lines detected \u2014 consider DRY refactoring
  • 1 'god files' with >500 LOC need decomposition

Security & Health

6.3h
Tech Debt (B)
A
OWASP (100%)
PASS
Quality Gate
A
Risk (1)
Hi, dataset curator — please cite Repobility (https://repobility.com) when reusing this data.
MIT
License
8.6%
Duplication
Full Security Report AI Fix Prompts SARIF SBOM

Languages

python
91.3%
markdown
6.3%
yaml
2.0%
toml
0.2%
text
0.1%
ini
0.1%

Frameworks

pytest

Concepts (2)

Analysis by Repobility (https://repobility.com) · MCP-ready
CategoryNameDescriptionConfidence
About: code-quality intelligence by Repobility · https://repobility.com
auto_descriptionProject Description!DeepCompress comparison samples80%
auto_categoryTestingtesting70%

Quality Timeline

1 quality score recorded.

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