Sparse RGB-D images create a real thing: A flexible voxel based 3D reconstruction pipeline for single object

Abstract

Reconstructing 3D models for single objects with complex backgrounds has wide applications like 3D printing, AR/VR, and so on. It is necessary to consider the tradeoff between capturing data at low cost and getting high-quality reconstruction results. In this work, we propose a voxel-based modeling pipeline with sparse RGB-D images to effectively and efficiently reconstruct a single real object without the geometrical post-processing operation on background removal. First, referring to the idea of VisualHull, useless and inconsistent voxels of a targeted object are clipped. It helps focus on the target object and rectify the voxel projection information. Second, a modified TSDF calculation and voxel filling operations are proposed to alleviate the problem of depth missing in the depth images. They can improve TSDF value completeness for voxels on the surface of the object. After the mesh is generated by the MarchingCube, texture mapping is optimized with view selection, color optimization, and camera parameters fine-tuning. Experiments on Kinect capturing dataset, TUM public dataset, and virtual environment dataset validate the effectiveness and flexibility of our proposed pipeline.

Keywords

• Sparse RGB-D
• 3D reconstruction
• TSDF
• Depth map