Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based Localization

• URL: http://arxiv.org/abs/2404.11064v3
• Date: Wed, 18 Dec 2024 04:23:29 GMT
• Title: Rethinking 3D Dense Caption and Visual Grounding in A Unified Framework through Prompt-based Localization
• Authors: Yongdong Luo, Haojia Lin, Xiawu Zheng, Yigeng Jiang, Fei Chao, Jie Hu, Guannan Jiang, Songan Zhang, Rongrong Ji,
• Abstract summary: 3D Visual Grounding (3DVG) and 3D Captioning (3DDC) are two crucial tasks in various 3D applications. We propose a unified framework, 3DGCTR, to jointly solve these two distinct but closely related tasks. In terms of implementation, we integrate a Lightweight Caption Head into the existing 3DVG network with a Caption Text Prompt as a connection.
• Score: 51.33923845954759
• License:
• Abstract: 3D Visual Grounding (3DVG) and 3D Dense Captioning (3DDC) are two crucial tasks in various 3D applications, which require both shared and complementary information in localization and visual-language relationships. Therefore, existing approaches adopt the two-stage "detect-then-describe/discriminate" pipeline, which relies heavily on the performance of the detector, resulting in suboptimal performance. Inspired by DETR, we propose a unified framework, 3DGCTR, to jointly solve these two distinct but closely related tasks in an end-to-end fashion. The key idea is to reconsider the prompt-based localization ability of the 3DVG model. In this way, the 3DVG model with a well-designed prompt as input can assist the 3DDC task by extracting localization information from the prompt. In terms of implementation, we integrate a Lightweight Caption Head into the existing 3DVG network with a Caption Text Prompt as a connection, effectively harnessing the existing 3DVG model's inherent localization capacity, thereby boosting 3DDC capability. This integration facilitates simultaneous multi-task training on both tasks, mutually enhancing their performance. Extensive experimental results demonstrate the effectiveness of this approach. Specifically, on the ScanRefer dataset, 3DGCTR surpasses the state-of-the-art 3DDC method by 4.3% in CIDEr@0.5IoU in MLE training and improves upon the SOTA 3DVG method by 3.16% in Acc@0.25IoU. The codes are at https://github.com/Leon1207/3DGCTR.

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