  Autonomous assembly in robotics and 3D vision presents significant
challenges, particularly in ensuring assembly correctness. Presently,
predominant methods such as MEPNet focus on assembling components based on
manually provided images. However, these approaches often fall short in
achieving satisfactory results for tasks requiring long-term planning.
Concurrently, we observe that integrating a self-correction module can
partially alleviate such issues. Motivated by this concern, we introduce the
single-step assembly error correction task, which involves identifying and
rectifying misassembled components. To support research in this area, we
present the LEGO Error Correction Assembly Dataset (LEGO-ECA), comprising
manual images for assembly steps and instances of assembly failures.
Additionally, we propose the Self-Correct Assembly Network (SCANet), a novel
method to address this task. SCANet treats assembled components as queries,
determining their correctness in manual images and providing corrections when
necessary. Finally, we utilize SCANet to correct the assembly results of
MEPNet. Experimental results demonstrate that SCANet can identify and correct
MEPNet's misassembled results, significantly improving the correctness of
assembly. Our code and dataset are available at
https://github.com/Yaser-wyx/SCANet.
