Fine-Grained VR Sketching: Dataset and Insights

TL;DR

This paper introduces a new fine-grained 3D VR sketch dataset of chairs, enabling detailed analysis and retrieval, and provides insights to guide future research in VR sketching and shape reconstruction.

Contribution

It presents the first fine-grained 3D VR sketch dataset for chairs and explores its application in 3D shape retrieval, offering new insights for the VR sketching community.

Findings

The dataset contains 1,497 sketch-shape pairs with high shape diversity.

Sparse line sketches without prior training are effective for shape retrieval.

Insights on design factors influencing VR sketch-based 3D shape retrieval.

Abstract

We present the first fine-grained dataset of 1,497 3D VR sketch and 3D shape pairs of a chair category with large shapes diversity. Our dataset supports the recent trend in the sketch community on fine-grained data analysis, and extends it to an actively developing 3D domain. We argue for the most convenient sketching scenario where the sketch consists of sparse lines and does not require any sketching skills, prior training or time-consuming accurate drawing. We then, for the first time, study the scenario of fine-grained 3D VR sketch to 3D shape retrieval, as a novel VR sketching application and a proving ground to drive out generic insights to inform future research. By experimenting with carefully selected combinations of design factors on this new problem, we draw important conclusions to help follow-on work. We hope our dataset will enable other novel applications, especially those that require a fine-grained angle such as fine-grained 3D shape reconstruction. The dataset is available at tinyurl.com/VRSketch3DV21.