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@@ -82,26 +82,26 @@ <h1 class="title is-1 publication-title">Solving Zero-Shot 3D Visual Grounding a | |
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3D visual grounding (3DVG) aims to locate objects in a 3D scene with natural language descriptions. | ||
Supervised methods have achieved decent accuracy, but have a closed vocabulary and limited language | ||
understanding ability. Zero-shot methods mostly utilize large language models (LLMs) to handle natural | ||
language descriptions, yet suffer from slow inference speed. To address these problems, in this work, we | ||
propose a zero-shot method that reformulates the 3DVG task as a Constraint Satisfaction Problem (CSP), | ||
where the variables and constraints represent objects and their spatial relations, respectively. This | ||
allows a global reasoning of all relevant objects, producing grounding results of both the target and | ||
anchor objects. Moreover, we demonstrate the flexibility of our framework by handling negation- and | ||
counting-based queries with only minor extra coding efforts. Our system, Constraint Satisfaction Visual | ||
Grounding (CSVG), has been extensively evaluated on the public datasets ScanRefer and Nr3D datasets using | ||
only open-source LLMs. Results show the effectiveness of CSVG and superior grounding accuracy over current | ||
state-of-the-art zero-shot 3DVG methods with improvements of +7.0% ([email protected] score) and +11.2% on the | ||
ScanRefer and Nr3D datasets, respectively. | ||
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