NCT Depth Motif: Exploring Symbolic 3D Motifs for RGB-D Depth Maps
Source: Dev.to
Overview
I just published the first technical release of NCT Depth Motif, an exploratory computer‑vision project focused on RGB‑D / depth‑map validation.
Repository
https://github.com/Hanzzel-corp/nct-depth-motif
Method
NCT Depth Motif tests whether local depth‑map structure can be represented as discrete 3D symbolic motifs across the X/Y/Z components.
Instead of treating depth maps only as continuous gradients, the method discretizes local geometric behavior into motif states and evaluates whether those motifs survive statistically against random baselines.
Experiments
- RGB‑D / depth‑map experiments
- NCT 3D motif‑survival validation
- Grouped split validation
- RGB‑cluster leave‑one‑out validation
- CUDA‑accelerated random baseline evaluation
- Empirical p‑values
- Reproducibility scripts
- Documented limitations
Strongest Evaluated Variant
motif_survival_binary – in the current exploratory setup, it showed a consistent positive signal against random motif baselines.
Note: This is not a claim of state‑of‑the‑art performance, nor a peer‑reviewed result. The effect is statistically consistent but modest in magnitude. The goal of this release is reproducibility, falsifiability, and technical feedback.
Limitations
- Modest effect size
- Not peer‑reviewed
- Limited baseline comparisons
Feedback Requested
I am interested in feedback around:
- The validation design
- The random baseline setup
- The grouped split methodology
- RGB‑cluster leave‑one‑out validation
- Possible classical baselines to compare against
- Ways to make the experiment more rigorous
Related Work
This project is part of my broader work around NCT — Números Cuánticos Tridimensionales — and symbolic/geometric representations for AI and computer vision.
Call for Input
If you work with computer vision or RGB‑D datasets, which baseline would you add first?
- Sobel / Canny
- HED
- Normal‑based edges
- Learned depth‑edge models
Feedback is welcome.