CNC manufacturing is a process that employs computer numerical control (CNC) machines to govern the move-ments of various industrial tools and machinery, encom-passing equipment ranging from grinders and lathes to mills and CNC routers. However, the reliance on man-ual CNC programming has become a bottleneck, and the requirement for expert knowledge can result in significant costs. Therefore, we introduce a pioneering approach named CNC-Net, representing the use of deep neural net-works (DNNs) to simulate CNC machines and grasp intri-cate operations when supplied with raw materials. CNC-Net constitutes a self-supervised framework that exclu-sively takes an input 3D model and subsequently gener-ates the essential operation parameters required by the CNC machine to construct the object. Our method has the potential to transformative automation in manufac-turing by offering a cost-effective alternative to the high costs of manual CNC programming while maintaining ex-ceptional precision in 3D object production. Our ex-periments underscore the effectiveness of our CNC-Net in constructing the desired 3D objects through the uti-lization of CNC operations. Notably, it excels in pre-serving finer local details, exhibiting a marked enhance-ment in precision compared to the state-of-the-art 3D CAD reconstruction approaches. The codes are available at https://github.com/myavartanoo/CNC-Net_PyTorch.

Point cloud completion addresses filling in the missing parts of a partial point cloud obtained from depth sensors and generating a complete point cloud. Although there has been steep progress in the supervised methods on the synthetic point cloud completion task, it is hardly applicable in real-world scenarios due to the domain gap between the synthetic and real-world datasets or the requirement of prior information. To overcome these limitations, we propose a novel self-supervised framework ACL-SPC for point cloud completion to train and test on the same data. ACL-SPC takes a single partial input and attempts to output the complete point cloud using an adaptive closed-loop (ACL) system that enforces the output same for the variation of an input. We evaluate our ACL-SPC on various datasets to prove that it can successfully learn to complete a partial point cloud as the first self-supervised scheme. Results show that our method is comparable with unsupervised methods and achieves superior performance on the real-world dataset compared to the supervised methods trained on the synthetic dataset. Extensive experiments justify the necessity of self-supervised learning and the effectiveness of our proposed method for the real-world point cloud completion task. The code is publicly available from this link.