NegROI: Click-Centric Uncertainty-Guided Refinement with Scene-Conditioned Negative Prompts for Robust Interactive 3D Segmentation
arXiv:2607.05955v1 Announce Type: cross Abstract: Interactive 3D segmentation aims to extract object masks in point clouds with minimal user clicks. Despite recent progress, most existing approaches still struggle with (i) coarse voxel resolution that blurs fine boundaries under limited clicks and (ii) hard false positives caused by confusing background structures. These issues are exacerbated by density and scale shifts across datasets (e.g., dense RGB-D reconstructions vs. sparse LiDAR scans), where fixed refinement heuristics and purely click-driven decoding generalize poorly. To address th
The continuous evolution of AI segmentation techniques, particularly for 3D data, addresses persistent challenges in accuracy and user interaction, reflecting ongoing innovation in computer vision.
Improved 3D segmentation with minimal user input is critical for automating tasks in robotics, virtual reality, and medical imaging, reducing operational costs and increasing efficiency.
This advancement changes how interactively 3D environments are understood and manipulated, making object extraction more robust across diverse and challenging datasets.
- · AI/ML researchers
- · Robotics industry
- · AR/VR developers
- · Medical imaging
More accurate and efficient 3D object recognition and manipulation across various applications.
Accelerated development of autonomous systems that rely on precise 3D environment understanding.
Reduced need for human intervention in highly complex 3D data annotation and interaction tasks, leading to further automation.
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Read at arXiv cs.AI