As electronic devices become increasingly compact, designing printed circuit boards (PCBs) has become more challenging, particularly in the routing step, which is now more complex and time-consuming. In this work, we propose a method that applies generative pretrained transformers (GPTs) for PCB routing, referred to as GPCB routing. Initially, we convert the detailed routing information of the PCB into network flow-based encodings. Consequently, GPCB routing tokenizes routing patterns, effectively transforming the routing task into a form of token encoding prediction. To enhance prediction accuracy, we implement a 2-D sliding window with a local memory scheme, thereby expanding the sensing area of GPCB. Additionally, we propose a multi-information fusion scheme to identify the start and end points of multiple wires to further improve the prediction accuracy. Compared to existing routing methods, GPCB has the distinct advantage of learning routing strategies from human experts, breaking the limitations of traditional model-based routing approaches. Moreover, GPCB operates as a parallel routing method capable of predicting multiple routes simultaneously, resulting in significant enhancements in routing performance. Based on the experimental results, GPCB consistently outperforms in terms of routability, runtime, and wirelength.

Self-supervised depth estimation has shown great prospects in inferring 3D structures using purely unannotated images. However, its performance usually drops when trained on the images with changing brightness and moving objects. In this paper, we address this issue by enhancing the robustness of the self-supervised paradigm using a set of image-based and geometry-based constraints. Our contributions are threefold, 1) we propose a gradient-based robust photometric loss which restrains the false supervisory signals caused by brightness changes, 2) we propose to filter out the unreliable areas that violate the rigid assumption by a novel combined selective mask, which is computed on the forward pass of the network by leveraging the inter-loss consistency and the loss-gradient consistency, and 3) we constrain the motion estimation network to generate across-frame consistent motions via proposing a triplet-based cycle consistency constraint. Extensive experiments conducted on KITTI, Cityscape and Make3D datasets demonstrate the superiority of our method, that the proposed method can effectively handle complex scenes with changing brightness and object motions. Both qualitative and quantitative results show that the proposed method outperforms the state-of-the-art methods.