Multi-3D-Models Registration-Based Augmented Reality (AR) Instructions for Assembly
It addresses the problem of providing robust AR assembly instructions for users in fields like manufacturing and construction, though it appears incremental as it builds on existing AR and deep learning methods.
This paper tackles the challenge of providing markerless, step-by-step 3D AR instructions for small parts assembly by introducing BRICKxAR (M3D), which uses deep learning-trained 3D model-based registration and a multi-phase approach to simplify complex assemblies and improve object recognition, with testing and evaluation conducted through user and expert qualitative analysis.
This paper introduces a novel, markerless, step-by-step, in-situ 3D Augmented Reality (AR) instruction method and its application - BRICKxAR (Multi 3D Models/M3D) - for small parts assembly. BRICKxAR (M3D) realistically visualizes rendered 3D assembly parts at the assembly location of the physical assembly model (Figure 1). The user controls the assembly process through a user interface. BRICKxAR (M3D) utilizes deep learning-trained 3D model-based registration. Object recognition and tracking become challenging as the assembly model updates at each step. Additionally, not every part in a 3D assembly may be visible to the camera during the assembly. BRICKxAR (M3D) combines multiple assembly phases with a step count to address these challenges. Thus, using fewer phases simplifies the complex assembly process while step count facilitates accurate object recognition and precise visualization of each step. A testing and heuristic evaluation of the BRICKxAR (M3D) prototype and qualitative analysis were conducted with users and experts in visualization and human-computer interaction. Providing robust 3D AR instructions and allowing the handling of the assembly model, BRICKxAR (M3D) has the potential to be used at different scales ranging from manufacturing assembly to construction.