arXiv:2606.18429v1 Announce Type: cross Abstract: Accurately aligning CAD models to their corresponding objects in indoor RGB-D scans is a central challenge in 3D semantic reconstruction. The task requires estimating a 9-Degree-of-Freedom (DoF) pose-position, rotation, and scale along three axes-but is hindered by noisy and incomplete scans, as well as segmentation errors that cause geometric distortions. We present Completion-Assisted Object-CAD Alignment (CAOA), a method that integrates a semantically and contextually aware point cloud completion module with a symmetry-aware relative pose es
The continuous advancements in AI and 3D vision, coupled with the increasing availability of RGB-D scanning technology, drive the need for more robust and accurate 3D semantic reconstruction methods.
Improved 3D object-CAD alignment is critical for applications across robotics, augmented reality, industrial automation, and digital twins, enabling greater precision and efficiency in real-world deployments.
The ability to accurately align CAD models to real-world objects in noisy and incomplete scans will significantly enhance the robustness and practical utility of 3D reconstruction systems, reducing errors and manual intervention.
- · Robotics sector
- · Augmented Reality companies
- · Industrial automation firms
- · 3D scanning and computer vision providers
- · Manual alignment services
- · Companies reliant on less accurate 3D reconstruction methods
This method directly improves the accuracy and reliability of 3D semantic reconstruction in challenging real-world environments.
Enhanced 3D reconstruction capabilities will accelerate the development and deployment of advanced robotics and mixed reality applications that require precise object interaction.
The widespread adoption of highly accurate 3D alignment could enable more sophisticated digital twin applications and automated manufacturing processes.
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Read at arXiv cs.AI