Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images

• URL: http://arxiv.org/abs/2103.07894v3
• Date: Wed, 17 Mar 2021 06:35:20 GMT
• Title: Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images
• Authors: Haolin Liu, Anran Lin, Xiaoguang Han, Lei Yang, Yizhou Yu, Shuguang Cui
• Abstract summary: Grounding referring expressions in RGBD image has been an emerging field. We present a novel task of 3D visual grounding in single-view RGBD image where the referred objects are often only partially scanned due to occlusion. Our approach first fuses the language and the visual features at the bottom level to generate a heatmap that localizes the relevant regions in the RGBD image. Then our approach conducts an adaptive feature learning based on the heatmap and performs the object-level matching with another visio-linguistic fusion to finally ground the referred object.
• Score: 69.5662419067878
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Grounding referring expressions in RGBD image has been an emerging field. We present a novel task of 3D visual grounding in single-view RGBD image where the referred objects are often only partially scanned due to occlusion. In contrast to previous works that directly generate object proposals for grounding in the 3D scenes, we propose a bottom-up approach to gradually aggregate context-aware information, effectively addressing the challenge posed by the partial geometry. Our approach first fuses the language and the visual features at the bottom level to generate a heatmap that coarsely localizes the relevant regions in the RGBD image. Then our approach conducts an adaptive feature learning based on the heatmap and performs the object-level matching with another visio-linguistic fusion to finally ground the referred object. We evaluate the proposed method by comparing to the state-of-the-art methods on both the RGBD images extracted from the ScanRefer dataset and our newly collected SUNRefer dataset. Experiments show that our method outperforms the previous methods by a large margin (by 11.2% and 15.6% Acc@0.5) on both datasets.

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