Search arXiv⌕ Search
Home/Archive/arXiv

arXiv · 2203.02252

Parametric/direct CAD integration

Qiang Zou
View original source ↗Read source document ↗Export metadata ↓

Also available from

Abstract

In the history of computer-aided design (CAD), feature-based parametric modeling and boundary representation-based direct modeling are two of the most important CAD paradigms, developed respectively in the late 1980s and the late 2000s. They have complementary advantages and limitations, thereby offering huge potential for improvement towards an integrated CAD modeling scheme. Some believe that their integration will be the key characteristic of next generation CAD software. This paper provides a brief review on current parametric/direct integration approaches. Their basic ideas, advantages, and disadvantages will be discussed. The main result reads that existing integration approaches are far from being completed if seamless parametric/direct integration is desired. It is hoped that, by outlining what has already been made possible and what still remains problematic, more researchers will be attracted to work on this very important research topic of parametric/direct integration. This paper serves as a complement to the CAD paper titled ``Variational Direct Modeling: A Framework Towards Integration of Parametric Modeling and Direct Modeling in CAD." Cite this work as follows: Qiang Zou, Hsi-Yung Feng, and Shuming Gao. Variational Direct Modeling: A Framework Towards Integration of Parametric Modeling and Direct Modeling in CAD. Computer-Aided Design 157 (2023): 103465.

Explore related subjects

cs.GRcs.CG

Keep this discovery

BibTeXRIS

Qiang Zou. 2022-03-04. Parametric/direct CAD integration. https://doi.org/10.1016/j.cad.2022.103465

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

arXiv2026

ReCHOIR: Contact-guided Human Object Interaction Retargeting to Diverse Characters

We present ReCHOIR, a novel contact-guided motion retargeting method for transferring human object interaction (HOI) motions across diverse humanoid characters. Unlike prior motion retargeting methods that primarily focus on transferring human motion alone, our goal is to preserve not only the semantics of the original body movement but also consistent interaction between the character and the manipulated object, while jointly producing aligned target human and object motions. Given source HOI motion, object geometry, and contact cues extracted from the source interaction, ReCHOIR retargets an HOI sequence to target characters with different skeletal configurations while maintaining both motion semantics and contact-consistent interaction patterns. Our method builds on a Part-Aware Motion Embedding (PAME) autoencoder, which encodes full-body motion into a shared body-part-wise latent space. This representation enables generalization across heterogeneous skeletons while preserving local motion semantics beneficial for part-aware adaptation in HOI retargeting. On top of this representation, we introduce a contact-guided retargeting module and an object motion decoder for HOI retargeting. The contact-guided retargeting module treats the source object interaction as a condition for refining target character motion: object- and contact-related signals are encoded into a body-part-aligned latent representation and injected into decoding through a residual control branch, enabling stronger adaptation in interaction-relevant body regions without discarding the underlying motion prior. In parallel, the object motion decoder predicts a target object motion aligned with the refined target character motion, ensuring that the object trajectory remains consistent with how the interaction is realized by the target character.

cs.GR↗
arXiv2026

Gaussian Light Transport

We present a novel method for computing global illumination by expressing the solution to the light transport equation as a 13D Gaussian mixture model over positions, directions, surface normals, and material properties. We show that including scene properties in the Gaussian representation drastically reduces the number of functions and speeds up evaluation. As opposed to traditional light transport methods based on Neumann series, the parameters of our model are directly estimated by minimizing the residual of the rendering equation. While both optimization and rendering require repeated evaluations of a linear combination of high-dimensional Gaussian functions, we introduce an efficient culling strategy to keep the optimization tractable and produce renderings in real time. Our representation enables to render fast, view-independent solutions to the light transport equation, achieving rendering times on the order of milliseconds, with a fraction of the memory requirements of conventional neural rendering approaches.

cs.GR↗
arXiv2026

Hologram Representation via Quadratic Phase Gaussian Splatting

We introduce Complex-Valued Quadratic Phase Gaussian (CVQPG), a novel hologram representation method that replaces standard 2D Gaussian representations used in 2D Gaussian Splatting with 2D quadratic phase functions. CVQPG incorporates additional learnable parameters to control the curvature of these bases. We evaluate our approach against state-of-the-art methods, exceeding the visual quality by +0.19 dB (RGB) and +0.33 dB (grayscale) on average in holographic reconstructions. Specifically, our equal parameter count evaluations show that modulating the primitive's wavefront is an effective and lightweight enhancement for hologram representations. In addition, our frequency domain analysis illustrates that CVQPG has successfully preserved the mid-to-high frequency band of natural images.

cs.GR↗