  Due to their complex spatial structure and diverse geometric features,
achieving high-precision and robust point cloud registration for complex Die
Castings has been a significant challenge in the die-casting industry. Existing
point cloud registration methods primarily optimize network models using
well-established high-quality datasets, often neglecting practical application
in real scenarios. To address this gap, this paper proposes a high-precision
adaptive registration method called Multiscale Efficient Deep Closest Point
(MEDPNet) and introduces a die-casting point cloud dataset, DieCastCloud,
specifically designed to tackle the challenges of point cloud registration in
the die-casting industry. The MEDPNet method performs coarse die-casting point
cloud data registration using the Efficient-DCP method, followed by precision
registration using the Multiscale feature fusion dual-channel registration
(MDR) method. We enhance the modeling capability and computational efficiency
of the model by replacing the attention mechanism of the Transformer in DCP
with Efficient Attention and implementing a collaborative scale mechanism
through the combination of serial and parallel blocks. Additionally, we propose
the MDR method, which utilizes multilayer perceptrons (MLP), Normal
Distributions Transform (NDT), and Iterative Closest Point (ICP) to achieve
learnable adaptive fusion, enabling high-precision, scalable, and
noise-resistant global point cloud registration. Our proposed method
demonstrates excellent performance compared to state-of-the-art geometric and
learning-based registration methods when applied to complex die-casting point
cloud data.
