CAD-Deform: Deformable Fitting of CAD Models to 3D Scans

Ishimtsev, Vladislav; Bokhovkin, Alexey; Artemov, Alexey; Ignatyev, Savva; Nießner, Matthias; Zorin, Denis; Burnaev, Evgeny

doi:10.1007/978-3-030-58601-0_36

Cited by 15 publications

(5 citation statements)

References 34 publications

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“…CAD-deform [36] aims to deal with deformation between a scan and CAD model. It can obtain more accurate CAD-to-scan fits by non-rigidly deforming retrieved CAD models.…”

Section: Related Workmentioning

confidence: 99%

Shape embedding and retrieval in multi-flow deformation

Leng,

Huang,

Shen

et al. 2024

Comp. Visual Media

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We propose a unified 3D flow framework for joint learning of shape embedding and deformation for different categories. Our goal is to recover shapes from imperfect point clouds by fitting the best shape template in a shape repository after deformation. Accordingly, we learn a shape embedding for template retrieval and a flow-based network for robust deformation. We note that the deformation flow can be quite different for different shape categories. Therefore, we introduce a novel multi-hub module to learn multiple modes of deformation to incorporate such variation, providing a network which can handle a wide range of objects from different categories. The shape embedding is designed to retrieve the best-fit template as the nearest neighbor in a latent space. We replace the standard fully connected layer with a tiny structure in the embedding that significantly reduces network complexity and further improves deformation quality. Experiments show the superiority of our method to existing state-of-the-art methods via qualitative and quantitative comparisons. Finally, our method provides efficient and flexible deformation that can further be used for novel shape design.

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“…CAD-deform [36] aims to deal with deformation between a scan and CAD model. It can obtain more accurate CAD-to-scan fits by non-rigidly deforming retrieved CAD models.…”

Section: Related Workmentioning

confidence: 99%

Shape embedding and retrieval in multi-flow deformation

Leng,

Huang,

Shen

et al. 2024

Comp. Visual Media

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show abstract

“…In other words, the retrieved CAD model should replicate the object shape as closely as possible. In contrast to the works of Ishimtsev et al [34] and Uy et al [35], our method avoids the involvement of expensive CAD model deformation, and makes full use of a large-scale database to identify the model that is most similar to the scanned object.…”

Section: A Overviewmentioning

confidence: 99%

Accurate Instance-Level CAD Model Retrieval in a Large-Scale Database

Wei¹,

Huang²,

Wang³

et al. 2022

Preprint

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“…Their work is also limited to simple shapes like cylinders. A similar work by Ishimtsev et al [29] has introduced a method CAD-to-scan that fits by non-rigidly deforming retrieved CAD models. A nonrigid deformation model incorporating smooth transformations and preservation of sharp features, that simultaneously achieves very tight fits from CAD models to the 3D scan and maintains the clean, high-quality surface properties of handmodeled CAD objects.…”

Section: Prior Workmentioning

confidence: 99%

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

Shah

Pernot

Polette

et al. 2021

Journal of Computing and Information Science in Engineering

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This paper introduces a novel reverse engineering technique for the reconstruction of editable CAD models of mechanical parts' assemblies. The input is a point cloud of a mechanical parts' assembly that has been acquired as a whole, i.e. without disassembling it prior to its digitization. The proposed framework allows for the reconstruction of the parametric CAD assembly model through a multi-step reconstruction and fitting approach. It is modular and it supports various exploitation scenarios depending on the available data and starting point. It also handles incomplete datasets. The reconstruction process starts from roughly sketched and parameterized geometries (i.e 2D sketches, 3D parts or assemblies) that are then used as input of a simulated annealing-based fitting algorithm, which minimizes the deviation between the point cloud and the reconstructed geometries. The coherence of the CAD models is maintained by a CAD modeler that performs the updates and satisfies the geometric constraints as the fitting process goes on. The optimization process leverages a two-level filtering technique able to capture and manage the boundaries of the geometries inside the overall point cloud in order to allow for local fitting and interfaces detection. It is a user-driven approach where the user decides what are the most suitable steps and sequence to operate. It has been tested and validated on both real scanned point clouds and as-scanned virtually generated point clouds incorporating several artifacts that would appear with real acquisition devices.

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CAD-Deform: Deformable Fitting of CAD Models to 3D Scans

Cited by 15 publications

References 34 publications

Shape embedding and retrieval in multi-flow deformation

Shape embedding and retrieval in multi-flow deformation

Accurate Instance-Level CAD Model Retrieval in a Large-Scale Database

User-Driven Computer-Assisted Reverse Engineering of Editable CAD Assembly Models

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