From the Physics Lab
to Production AI.
Segmentum was born at the Princeton University Physics Department. Our research focuses on a fundamental shift: moving away from segmentation as a pixel-classification problem and toward segmentation as a geometric reasoning task.
The Core Insight
Modern "State-of-the-Art" (SOTA) models are primarily trained on massive, 2D-centric datasets. While they are impressive at identifying common objects, they lack an intrinsic understanding of **spatial manifolding**.
By applying principles of topological data analysis and physics-informed neural networks, we developed a system that understands how volumes occupy space. This allows Segmentum to generalize to entirely new visual domains with vanishingly small amounts of data.
Research Milestones
- Few-shot adaptation without retraining
- Cross-dimensional (2D to 3D) inference
- Geometric consistency across temporal frames
- Physics-constrained latent spaces
Shattering the SOTA Ceiling
In our internal testing, Segmentum consistently outperforms industry-standard foundational models across diverse benchmarks, particularly in low-data regimes.
White Paper: Spatial Geometry Transformers
We are finalizing a comprehensive white paper detailing our architectural breakthroughs, extensive benchmark results on major open datasets, and the mathematical framework behind our few-shot performance.