GraphCanon updated Sep 25, 2026 · GitHub synced Sep 25, 2026
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Decision brief
3D-Mem excels in environments where embodied AI needs spatial intelligence for exploration and reasoning in 3D scenes.
Good fit when
- Use if your project involves embodied AI systems that require detailed scene understanding in 3D
- Choose this tool when working with tasks like navigation or object manipulation based on 3D scene comprehension
Avoid when
- Avoid for environments where 2D image analysis suffices over deeper 3D spatial reasoning
- This may not be suitable if real-time performance is more critical than the richness of 3D scene intelligence
Observed Jul 16, 2026 · Source: enrich:decision_facts
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Maintenance and security
Full trust report- Maintenance
- Slowing (357d since push)
- As of Sep 25, 2026
- Provenance
- Not a fork · Organization account
- As of Sep 25, 2026
- Security (OSV)
- No lockfile
- As of Jul 11, 2026
Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.
Install
pip install 3D-MemSimilar tools
Same-category neighbours. No typed graph edges are catalogued for this tool yet.
Evidence and technical details
Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.
Overview
A repository that provides source codes for implementing 3D-Mem: 3D Scene Memory for Embodied Exploration and Reasoning; focuses on generating and utilizing spatial intelligence in scenes to enhance embodied AI systems.
Capability facts
- Languages
- python
Source: github.language · Sep 25, 2026
Categories
Compatibility
Sourced claims from the README excerpt - not unsourced marketing copy.
Source: README excerpt (regex_v1, Sep 25, 2026)
Set up the conda environment (Linux, Python 3.9):Source link
Tags
README
Installation Set up the conda environment (Linux, Python 3.9):
For agents
This page has a .md twin and JSON over the API.