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We present Real2Code, a novel approach to reconstructing articulated objects via code generation. Given visual observations of an object, we first reconstruct its part geometry using an image segmentation model and a shape completion model.…

Computer Vision and Pattern Recognition · Computer Science 2024-06-14 Zhao Mandi , Yijia Weng , Dominik Bauer , Shuran Song

Large Language Models (LLMs) have demonstrated impressive capabilities in a wide range of code generation tasks. However, generating code for certain domains remains challenging. One such domain is Computer-Aided Design (CAD) program, where…

Machine Learning · Computer Science 2026-03-10 Yan-Ying Chen , Dule Shu , Matthew Hong , Andrew Taber , Jonathan Li , Matthew Klenk

Multimodal Large Language Models (MLLMs) have made impressive progress in connecting vision and language, but they still struggle with spatial understanding and viewpoint-aware reasoning. Recent efforts aim to augment the input…

Computer Vision and Pattern Recognition · Computer Science 2026-03-19 Kevin Qu , Haozhe Qi , Mihai Dusmanu , Mahdi Rad , Rui Wang , Marc Pollefeys

Current large vision-language models (LVLMs) typically employ a connector module to link visual features with text embeddings of large language models (LLMs) and use end-to-end training to achieve multi-modal understanding in a unified…

Artificial Intelligence · Computer Science 2025-08-14 Zixian Guo , Ming Liu , Qilong Wang , Zhilong Ji , Jinfeng Bai , Lei Zhang , Wangmeng Zuo

Vision-Language-Action (VLA) models offer a compelling framework for tackling complex robotic manipulation tasks, but they are often expensive to train. In this paper, we propose a novel VLA approach that leverages the competitive…

Robotics · Computer Science 2025-12-23 Max Argus , Jelena Bratulic , Houman Masnavi , Maxim Velikanov , Nick Heppert , Abhinav Valada , Thomas Brox

This paper demonstrates that a progressively aligned language model can effectively bridge frozen vision encoders and large language models (LLMs). While the fundamental architecture and pre-training methods of vision encoders and LLMs have…

Computer Vision and Pattern Recognition · Computer Science 2024-06-04 Junfei Xiao , Zheng Xu , Alan Yuille , Shen Yan , Boyu Wang

Vision-language models (VLMs) have achieved remarkable success in scene understanding and perception tasks, enabling robots to plan and execute actions adaptively in dynamic environments. However, most multimodal large language models lack…

Robotics · Computer Science 2025-02-14 Guoqin Tang , Qingxuan Jia , Zeyuan Huang , Gang Chen , Ning Ji , Zhipeng Yao

Designing complex computer-aided design (CAD) models is often time-consuming due to challenges such as computational inefficiency and the difficulty of generating precise models. We propose a novel language-guided framework for industrial…

Artificial Intelligence · Computer Science 2025-05-27 Jianxing Liao , Junyan Xu , Yatao Sun , Maowen Tang , Sicheng He , Jingxian Liao , Shui Yu , Yun Li , Hongguan Xiao

In this paper, we propose Img2CAD, the first approach to our knowledge that uses 2D image inputs to generate CAD models with editable parameters. Unlike existing AI methods for 3D model generation using text or image inputs often rely on…

Computer Vision and Pattern Recognition · Computer Science 2024-10-07 Tianrun Chen , Chunan Yu , Yuanqi Hu , Jing Li , Tao Xu , Runlong Cao , Lanyun Zhu , Ying Zang , Yong Zhang , Zejian Li , Linyun Sun

Computer-Aided Design (CAD) plays a central role in engineering and manufacturing, making it possible to create precise and editable 3D models. Using a variety of sensor or user-provided data as inputs for CAD reconstruction can democratize…

Computer Vision and Pattern Recognition · Computer Science 2026-02-18 Maksim Kolodiazhnyi , Denis Tarasov , Dmitrii Zhemchuzhnikov , Alexander Nikulin , Ilya Zisman , Anna Vorontsova , Anton Konushin , Vladislav Kurenkov , Danila Rukhovich

Generating editable, parametric CAD models from a single image holds great potential to lower the barriers of industrial concept design. However, current multi-modal large language models (MLLMs) still struggle with accurately inferring 3D…

Computer Vision and Pattern Recognition · Computer Science 2025-10-21 Yinghui Wang , Xinyu Zhang , Peng Du

Recent developments in Multimodal Large Language Models (MLLMs) have significantly improved Vision-Language (VL) reasoning in 2D domains. However, extending these capabilities to 3D scene understanding remains a major challenge. Existing 3D…

Computer Vision and Pattern Recognition · Computer Science 2026-03-03 Haijier Chen , Bo Xu , Shoujian Zhang , Haoze Liu , Jiaxuan Lin , Jingrong Wang

This paper investigates a 2D to 3D image translation method with a straightforward technique, enabling correlated 2D X-ray to 3D CT-like reconstruction. We observe that existing approaches, which integrate information across multiple 2D…

Computer Vision and Pattern Recognition · Computer Science 2024-06-27 Abril Corona-Figueroa , Hubert P. H. Shum , Chris G. Willcocks

Vision-language models (VLMs) have shown promise in 2D medical image analysis, but extending them to 3D remains challenging due to the high computational demands of volumetric data and the difficulty of aligning 3D spatial features with…

Computer Vision and Pattern Recognition · Computer Science 2025-12-17 Yu Xin , Gorkem Can Ates , Kuang Gong , Wei Shao

Computer-aided design (CAD) tools are utilized in the manufacturing industry for modeling everything from cups to spacecraft. These programs are complex to use and typically require years of training and experience to master. Structured and…

Computer Vision and Pattern Recognition · Computer Science 2024-08-27 Zhe Yuan , Jianqi Shi , Yanhong Huang

A metric-accurate semantic 3D representation is essential for many robotic tasks. This work proposes a simple, yet powerful, way to integrate the 2D embeddings of a Vision-Language Model in a metric-accurate 3D representation at real-time.…

Robotics · Computer Science 2025-08-11 Christian Rauch , Björn Ellensohn , Linus Nwankwo , Vedant Dave , Elmar Rueckert

In this study, we uncover the unexpected efficacy of residual-based large language models (LLMs) as part of encoders for biomedical imaging tasks, a domain traditionally devoid of language or textual data. The approach diverges from…

Computer Vision and Pattern Recognition · Computer Science 2024-04-01 Zhixin Lai , Jing Wu , Suiyao Chen , Yucheng Zhou , Naira Hovakimyan

Text-based retrieval of Computer-Aided Design (CAD) models is a critical yet underexplored task for the reuse of legacy industrial designs. Existing CAD repositories are typically searched using filenames or directories, which limits the…

Computer Vision and Pattern Recognition · Computer Science 2026-05-08 Honghu Pan , Zibo Du , Daxiang Liu , Chengliang Liu , Xiaoling Luo

Recent medical vision-language models (VLMs) have shown promise in 2D medical image interpretation. However extending them to 3D medical imaging has been challenging due to computational complexities and data scarcity. Although a few recent…

Image and Video Processing · Electrical Eng. & Systems 2024-12-19 Changsun Lee , Sangjoon Park , Cheong-Il Shin , Woo Hee Choi , Hyun Jeong Park , Jeong Eun Lee , Jong Chul Ye

Remarkable progress in 2D Vision-Language Models (VLMs) has spurred interest in extending them to 3D settings for tasks like 3D Question Answering, Dense Captioning, and Visual Grounding. Unlike 2D VLMs that typically process images through…

Computer Vision and Pattern Recognition · Computer Science 2025-06-09 Haoyuan Li , Yanpeng Zhou , Yufei Gao , Tao Tang , Jianhua Han , Yujie Yuan , Dave Zhenyu Chen , Jiawang Bian , Hang Xu , Xiaodan Liang