Introduces a method to design structure-specific relational inductive biases for a base transformer architecture, enabling end-to-end transcription of documents with intrinsic structures, demonstrated on sheet music, shape drawings, and mechanical engineering drawings.
Abc: A big cad model dataset for geometric deep learning
4 Pith papers cite this work. Polarity classification is still indexing.
4
Pith papers citing it
citation-role summary
dataset 2
citation-polarity summary
verdicts
UNVERDICTED 4roles
dataset 2polarities
use dataset 2representative citing papers
Text2CAD-Bench supplies 600 dual-prompt examples across four geometric and domain levels to test LLMs on text-to-parametric CAD, finding solid basic performance but sharp drops on complex topology and advanced features.
EdgeFormer converts point cloud edge detection into local-patch point classification with a transformer and reports competitive results against six baselines.
RWoDSN extracts feature points from point clouds via a novel DSN descriptor and random walk graph analysis, reporting 22% higher recall than prior state-of-the-art with 0.784 precision.
citing papers explorer
-
A document is worth a structured record: Principled inductive bias design for document recognition
Introduces a method to design structure-specific relational inductive biases for a base transformer architecture, enabling end-to-end transcription of documents with intrinsic structures, demonstrated on sheet music, shape drawings, and mechanical engineering drawings.
-
Text2CAD-Bench: A Benchmark for LLM-based Text-to-Parametric CAD Generation
Text2CAD-Bench supplies 600 dual-prompt examples across four geometric and domain levels to test LLMs on text-to-parametric CAD, finding solid basic performance but sharp drops on complex topology and advanced features.
-
EdgeFormer: local patch-based edge detection transformer on point clouds
EdgeFormer converts point cloud edge detection into local-patch point classification with a transformer and reports competitive results against six baselines.
-
Random Walk on Point Clouds for Feature Detection
RWoDSN extracts feature points from point clouds via a novel DSN descriptor and random walk graph analysis, reporting 22% higher recall than prior state-of-the-art with 0.784 precision.