NeuSOGA3D: A Neuro-Symbolic Framework for Explainable 3D Geometric Reconstruction

arXiv:2609.20323 · cs.AI · Submitted 2026-09-17 · Read on arXiv

cs.AI

Submitted: 2026-09-17

Updated: 2026-09-18

Comments: Preprint. Community feedback and comments are welcome

License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/

Terminology

• Abstraction — 50× in this paper · explained in 1 episode
• Interpretability — 15× in this paper · explained in 15 episodes
• Partial Shape-Preserving Splines — 9× in this paper
• Benchmark — 8× in this paper · explained in 2 episodes
• Point Clouds — 7× in this paper · explained in 2 episodes
• Explainability — 6× in this paper · explained in 3 episodes
• Accuracy — 5× in this paper · explained in 1 episode
• Consistency — 5× in this paper · explained in 3 episodes
• Embeddings — 4× in this paper · explained in 2 episodes
• Generalization — 4× in this paper · explained in 13 episodes
• Latent Representation — 4× in this paper · explained in 2 episodes
• Latent Representations — 4× in this paper · explained in 3 episodes
• Computer Vision and Pattern Recognition — 3× in this paper
• Fidelity — 3× in this paper · explained in 2 episodes
• Robustness — 3× in this paper · explained in 5 episodes
• Structural Reasoning — 3× in this paper · explained in 2 episodes
• Adam — 2× in this paper · explained in 2 episodes
• CVF International Conference on Computer Vision — 2× in this paper
• Foundation Model — 2× in this paper · explained in 3 episodes
• Foundation Models — 2× in this paper · explained in 7 episodes
• Grounding — 2× in this paper · explained in 3 episodes
• Provenance — 2× in this paper · explained in 3 episodes
• Verifiability — 2× in this paper · explained in 2 episodes
• Active Inference — 1× in this paper · explained in 1 episode
• Core — 1× in this paper · explained in 2 episodes
• Diffusion Model — 1× in this paper · explained in 8 episodes
• Feature Extraction — 1× in this paper · explained in 2 episodes
• Reasoning Traces — 1× in this paper · explained in 3 episodes
• Representation Learning — 1× in this paper · explained in 2 episodes
• Segment Anything Model (SAM) — 1× in this paper · explained in 2 episodes
• Trace — 1× in this paper · explained in 2 episodes
• Transformer — 1× in this paper · explained in 2 episodes
• Transformer Architecture — 1× in this paper · explained in 2 episodes
• Unified Framework — 1× in this paper · explained in 5 episodes

Sources

• Relational inductive biases, deep learning, and graph networks
• Distilling Neuro-Symbolic Programs into 3D Multi-modal LLMs
• Text-to-CadQuery: A New Paradigm for CAD Generation with Scalable Large Model Capabilities

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