ShapeNet: An Information-Rich 3D Model Repository

arXiv 2015 34.4 method

TLDR

ShapeNet is a large-scale, richly-annotated repository of 3D CAD models organized under WordNet taxonomy for computer graphics and vision research.

Reasoning

The paper's strength lies in its massive scale and rich annotations, providing a valuable benchmark for geometric analysis. However, it is a dataset paper without novel methods or algorithms, and its limitations include potential annotation inconsistencies and lack of specific task focus.

Read-first score

Read-first score 34.4, weighted from topical fit, citation, graph, method, reproducibility, and recency signals. Original total remains 22.

Methodology quality 25%

Screens visible abstract and analysis fields for experiment, dataset, baseline, metric, and limitation evidence. markers=analysis,benchmark,dataset,metric

Reproducibility 25%

Screens links and visible text for paper, code, dataset, artifact, and repository signals. pdf=True; code=False; dataset=False; markers=dataset,repository

Recency 8%

20.8

Uses a gentle age decay so recent papers surface without erasing older foundations. 2015

Topical relevance 42%

14.7

Uses existing LLM keyword relevance scores normalized to 0-100. AI for CAD,computer-aided design,neural CAD,generative CAD,parametric CAD,B-Rep,constructive solid geometry,CSG,sketch extrusion,CAD generation,CAD reconstruction,text-to-CAD,image-to-CAD,point cloud to CAD,CAD program

Field roles

Candidate

Rank sensitivity

Stability: volatile; rank range: 122.

Keyword Scores

computer-aided design

AI for CAD

generative CAD

neural CAD

parametric CAD

CAD generation

CAD reconstruction

B-Rep

constructive solid geometry

CSG

sketch extrusion

text-to-CAD

image-to-CAD

point cloud to CAD

CAD program

Resources

Paper