A depth map-based collision detection method in VR environment

Jun Xiao; Zugen Yan; Xujuan Dong

doi:10.1117/12.3011274

8 November 2023 A depth map-based collision detection method in VR environment

Jun Xiao, Zugen Yan, Xujuan Dong

Proceedings Volume 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023); 129231C (2023) https://doi.org/10.1117/12.3011274
Event: 3rd International Conference on Artificial Intelligence, Virtual Reality and Visualization (AIVRV 2023), 2023, Chongqing, China

Abstract

The characteristics of machinery "three many and three complexes", i.e. many structures, many mechanisms, many parts, and complex types, structures, and movements, make it difficult for students to understand and digest the teachers' exciting lectures. The virtual assembly can help students to learn in a safe environment, but also to be able to get close to and assemble these complex mechanical structures to help them learn better. The collision detection technology between parts in virtual assembly is the main problem. Based on the Yixinghe engine, this paper proposes to use the depth map for subsequent collision judgments for parts that are in the virtual environment, after using the AABB enclosing box to do the initial collision judgments.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Jun Xiao, Zugen Yan, and Xujuan Dong "A depth map-based collision detection method in VR environment", Proc. SPIE 12923, Third International Conference on Artificial Intelligence, Virtual Reality, and Visualization (AIVRV 2023), 129231C (8 November 2023); https://doi.org/10.1117/12.3011274

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available