arXiv:2606.31148v1 Announce Type: cross Abstract: 3D Visual Grounding (3DVG) aims to localize target objects in 3D scenes given natural language descriptions. Existing approaches typically perform reasoning over the entire scene, leading to ambiguous predictions and high computational cost, especially in cluttered environments. We observe that many referential expressions rely on local spatial context and often correspond to restricted spatial regions rather than the full scene. Motivated by this insight, we propose PruneGround, an effective plug-and-play framework for 3DVG built upon three ke
The proliferation of 3D data and advanced AI models necessitates more efficient and accurate methods for visual grounding in complex environments.
This research addresses a key computational and accuracy bottleneck in 3D visual grounding, crucial for advancements in robotics, augmented reality, and virtual reality applications.
The proposed 'PruneGround' framework introduces a method to significantly reduce computational load and improve prediction accuracy in 3D visual grounding by focusing on local spatial contexts.
- · AI/ML researchers
- · Robotics companies
- · AR/VR developers
- · 3D vision hardware manufacturers
- · Inefficient 3D visual grounding architectures
- · Applications with high computational demands for scene understanding
More efficient and accurate 3D object localization will accelerate development of advanced AI applications in complex real-world environments.
Reduced computational requirements could enable wider deployment of sophisticated 3D visual grounding in edge devices and less powerful systems.
Improved spatial reasoning in AI systems could lead to a ' Cambrian explosion' of new robotic and augmented reality use cases previously constrained by processing power.
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Read at arXiv cs.CL