3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment

• URL: http://arxiv.org/abs/2308.04352v1
• Date: Tue, 8 Aug 2023 15:59:17 GMT
• Title: 3D-VisTA: Pre-trained Transformer for 3D Vision and Text Alignment
• Authors: Ziyu Zhu, Xiaojian Ma, Yixin Chen, Zhidong Deng, Siyuan Huang, Qing Li
• Abstract summary: 3D-VisTA is a pre-trained Transformer for 3D Vision and Text Alignment. ScanScribe is the first large-scale 3D scene-text pairs dataset for 3D-VL pre-training.
• Score: 44.00343134325925
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: 3D vision-language grounding (3D-VL) is an emerging field that aims to connect the 3D physical world with natural language, which is crucial for achieving embodied intelligence. Current 3D-VL models rely heavily on sophisticated modules, auxiliary losses, and optimization tricks, which calls for a simple and unified model. In this paper, we propose 3D-VisTA, a pre-trained Transformer for 3D Vision and Text Alignment that can be easily adapted to various downstream tasks. 3D-VisTA simply utilizes self-attention layers for both single-modal modeling and multi-modal fusion without any sophisticated task-specific design. To further enhance its performance on 3D-VL tasks, we construct ScanScribe, the first large-scale 3D scene-text pairs dataset for 3D-VL pre-training. ScanScribe contains 2,995 RGB-D scans for 1,185 unique indoor scenes originating from ScanNet and 3R-Scan datasets, along with paired 278K scene descriptions generated from existing 3D-VL tasks, templates, and GPT-3. 3D-VisTA is pre-trained on ScanScribe via masked language/object modeling and scene-text matching. It achieves state-of-the-art results on various 3D-VL tasks, ranging from visual grounding and dense captioning to question answering and situated reasoning. Moreover, 3D-VisTA demonstrates superior data efficiency, obtaining strong performance even with limited annotations during downstream task fine-tuning.

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