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1. Rough carving of 3D models with spatial augmented reality

   https://doi.org/10.1145/3328939.3328998  

   Hattab, Ammar; Taubin, Gabriel (June 2019, Proceedings of the ACM Symposium on Computational Fabrication (SCF'19))

   Carving is a subtractive process where we get the shape by removing materials. While most people can get roughly the right intended shape, it is usually challenging not to over-cut the model. We propose a method that helps an unskilled user to carve a rough physical replica of a 3D model using the minimum number of cuts while only using manual cutting tools. The method starts by analyzing the input 3D model and generates the minimum set of cutting steps that remove most of the material. Then using a projector, we project the instructions sequentially onto a block of material to guide the user in performing them. We use the projector-camera setup to 3D scan the object after cutting and automatically detect the changes to reflect them on the digital model. We demonstrate a complete system to support this operation and show several examples of manually carved 3D models while using the system. 

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2. Differentiable 3D CAD Programs for Bidirectional Editing

   https://doi.org/10.1111/cgf.14476  

   Cascaval, D.; Shalah, M.; Quinn, P.; Bodik, R.; Agrawala, M.; Schulz, A. (May 2022, Computer Graphics Forum)

   Abstract Modern CAD tools represent 3D designs not only as geometry, but also as a program composed of geometric operations, each of which depends on a set of parameters. Program representations enable meaningful and controlled shape variations via parameter changes. However, achieving desired modifications solely through parameter editing is challenging when CAD models have not been explicitly authored to expose select degrees of freedom in advance. We introduce a novel bidirectional editing system for 3D CAD programs. In addition to editing the CAD program, users can directly manipulate 3D geometry and our system infers parameter updates to keep both representations in sync. We formulate inverse edits as a set of constrained optimization objectives, returning plausible updates to program parameters that both match user intent and maintain program validity. Our approach implements an automatically differentiable domain‐specific language for CAD programs, providing derivatives for this optimization to be performed quickly on any expressed program. Our system enables rapid, interactive exploration of a constrained 3D design space by allowing users to manipulate the program and geometry interchangeably during design iteration. While our approach is not designed to optimize across changes in geometric topology, we show it is expressive and performant enough for users to produce a diverse set of design variants, even when the CAD program contains a relatively large number of parameters. 

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3. A Lineage-Based Referencing DSL for Computer-Aided Design

   https://doi.org/10.1145/3591223  

   Cascaval, Dan; Bodik, Rastislav; Schulz, Adriana (June 2023, Proceedings of the ACM on Programming Languages)

   3D Computer-Aided Design (CAD) modeling is ubiquitous in mechanical engineering and design. Modern CAD models are programs that produce geometry and can be used to implement high-level geometric changes by modifying input parameters. While there has been a surge of recent interest in program-based tooling for the CAD domain, one fundamental problem remains unsolved. CAD programs pass geometric arguments to operations using references, which are queries that select elements from the constructed geometry according to programmer intent. The challenge is designing reference semantics that can express programmer intent across all geometric topologies achievable with model parameters, including topologies where the desired elements are not present. In current systems, both users and automated tools may create invalid models when parameters are changed, as references to geometric elements are lost or silently and arbitrarily switched. While existing CAD systems use heuristics to attempt to infer user intent in cases of this undefined behavior, this best-effort solution is not suitable for constructing automated tools to edit and optimize CAD programs. We analyze the failure modes of existing referencing schemes and formalize a set of criteria on which to evaluate solutions to the CAD referencing problem. In turn, we propose a domain-specific language that exposes references as a first-class language construct, using user-authored queries to introspect element history and define references safely over all paths. We give a semantics for fine-grained element lineage that can subsequently be queried; and show that our language meets the desired properties. Finally, we provide an implementation of a lineage-based referencing system in a 2.5D CAD kernel, demonstrating realistic referencing scenarios and illustrating how our system safely represents models that cause reference breakage in existing CAD systems. 

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4. DeepCAD: A Deep Generative Network for Computer-Aided Design Models

   https://doi.org/10.1109/ICCV48922.2021.00670  

   Wu, Rundi; Xiao, Chang; Zheng, Changxi (October 2021, International Conference on Computer Vision)

   Deep generative models of 3D shapes have received a great deal of research interest. Yet, almost all of them generate discrete shape representations, such as voxels, point clouds, and polygon meshes. We present the first 3D generative model for a drastically different shape representation—describing a shape as a sequence of computer-aided design (CAD) operations. Unlike meshes and point clouds, CAD models encode the user creation process of 3D shapes, widely used in numerous industrial and engineering design tasks. However, the sequential and irregular structure of CAD operations poses significant challenges for existing 3D generative models. Drawing an analogy between CAD operations and natural language, we propose a CAD generative network based on the Transformer. We demonstrate the performance of our model for both shape autoencoding and random shape generation. To train our network, we create a new CAD dataset consisting of 178,238 models and their CAD construction sequences. We have made this dataset publicly available to promote future research on this topic. 

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5. Kaleidoscopic structured light

   https://doi.org/10.1145/3478513.3480524  

   Ahn, Byeongjoo; Gkioulekas, Ioannis; Sankaranarayanan, Aswin_C (December 2021, ACM Transactions on Graphics)

   Full surround 3D imaging for shape acquisition is essential for generating digital replicas of real-world objects. Surrounding an object we seek to scan with a kaleidoscope, that is, a configuration of multiple planar mirrors, produces an image of the object that encodes information from a combinatorially large number of virtual viewpoints. This information is practically useful for the full surround 3D reconstruction of the object, but cannot be used directly, as we do not know what virtual viewpoint each image pixel corresponds---the pixel label. We introduce a structured light system that combines a projector and a camera with a kaleidoscope. We then prove that we can accurately determine the labels of projector and camera pixels, for arbitrary kaleidoscope configurations, using the projector-camera epipolar geometry. We use this result to show that our system can serve as a multi-view structured light system with hundreds of virtual projectors and cameras. This makes our system capable of scanning complex shapes precisely and with full coverage. We demonstrate the advantages of the kaleidoscopic structured light system by scanning objects that exhibit a large range of shapes and reflectances. 

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