Conceptual design is the foundational stage of a design process that translates ill-defined design problems into low-fidelity design concepts and prototypes through design search, creation, and integration. In this stage, product shape design is one of the most paramount aspects. When applying deep learning-based methods to product shape design, two major challenges exist: (1) design data exhibit in multiple modalities and (2) an increasing demand for creativity. With recent advances in deep learning of cross-modal tasks (DLCMTs), which can transfer one design modality to another, we see opportunities to develop artificial intelligence (AI) to assist the design of product shapes in a new paradigm. In this paper, we conduct a systematic review of the retrieval, generation, and manipulation methods for DLCMT that involve three cross-modal types: text-to-3D shape, text-to-sketch, and sketch-to-3D shape. The review identifies 50 articles from a pool of 1341 papers in the fields of computer graphics, computer vision, and engineering design. We review (1) state-of-the-art DLCMT methods that can be applied to product shape design and (2) identify the key challenges, such as lack of consideration of engineering performance in the early design phase that need to be addressed when applying DLCMT methods. In the end, we discuss the potential solutions to these challenges and propose a list of research questions that point to future directions of data-driven conceptual design.
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Cross-Modal Center Loss for 3D Cross-Modal Retrieval
Cross-modal retrieval aims to learn discriminative and modal-invariant features for data from different modalities. Unlike the existing methods which usually learn from the features extracted by offline networks, in this paper, we pro- pose an approach to jointly train the components of cross- modal retrieval framework with metadata, and enable the network to find optimal features. The proposed end-to-end framework is updated with three loss functions: 1) a novel cross-modal center loss to eliminate cross-modal discrepancy, 2) cross-entropy loss to maximize inter-class variations, and 3) mean-square-error loss to reduce modality variations. In particular, our proposed cross-modal center loss minimizes the distances of features from objects belonging to the same class across all modalities. Extensive experiments have been conducted on the retrieval tasks across multi-modalities including 2D image, 3D point cloud and mesh data. The proposed framework significantly outperforms the state-of-the-art methods for both cross-modal and in-domain retrieval for 3D objects on the ModelNet10 and ModelNet40 datasets.
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- Award ID(s):
- 2041307
- NSF-PAR ID:
- 10279248
- Date Published:
- Journal Name:
- IEEE Conference on Computer Vision and Pattern Recognition (CVPR)
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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Abstract Conceptual design is the foundational stage of a design process, translating ill-defined design problems to low-fidelity design concepts and prototypes. While deep learning approaches are widely applied in later design stages for design automation, we see fewer attempts in conceptual design for three reasons: 1) the data in this stage exhibit multiple modalities: natural language, sketches, and 3D shapes, and these modalities are challenging to represent in deep learning methods; 2) it requires knowledge from a larger source of inspiration instead of focusing on a single design task; and 3) it requires translating designers’ intent and feedback, and hence needs more interaction with designers and/or users. With recent advances in deep learning of cross-modal tasks (DLCMT) and the availability of large cross-modal datasets, we see opportunities to apply these learning methods to the conceptual design of product shapes. In this paper, we review 30 recent journal articles and conference papers across computer graphics, computer vision, and engineering design fields that involve DLCMT of three modalities: natural language, sketches, and 3D shapes. Based on the review, we identify the challenges and opportunities of utilizing DLCMT in 3D shape concepts generation, from which we propose a list of research questions pointing to future research directions.
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