Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding

• URL: http://arxiv.org/abs/2407.09781v1
• Date: Sat, 13 Jul 2024 05:39:17 GMT
• Title: Dense Multimodal Alignment for Open-Vocabulary 3D Scene Understanding
• Authors: Ruihuang Li, Zhengqiang Zhang, Chenhang He, Zhiyuan Ma, Vishal M. Patel, Lei Zhang,
• Abstract summary: We propose a Multimodal Alignment (DMA) framework to densely co-embed different modalities into a common space. Our DMA method produces highly competitive open-vocabulary segmentation performance on various indoor and outdoor tasks.
• Score: 39.55810156545949
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Recent vision-language pre-training models have exhibited remarkable generalization ability in zero-shot recognition tasks. Previous open-vocabulary 3D scene understanding methods mostly focus on training 3D models using either image or text supervision while neglecting the collective strength of all modalities. In this work, we propose a Dense Multimodal Alignment (DMA) framework to densely co-embed different modalities into a common space for maximizing their synergistic benefits. Instead of extracting coarse view- or region-level text prompts, we leverage large vision-language models to extract complete category information and scalable scene descriptions to build the text modality, and take image modality as the bridge to build dense point-pixel-text associations. Besides, in order to enhance the generalization ability of the 2D model for downstream 3D tasks without compromising the open-vocabulary capability, we employ a dual-path integration approach to combine frozen CLIP visual features and learnable mask features. Extensive experiments show that our DMA method produces highly competitive open-vocabulary segmentation performance on various indoor and outdoor tasks.

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