CuFusion2: Accurate and Denoised Volumetric 3D Object Reconstruction Using Depth Cameras

Abstract

The 3D object reconstruction from depth image streams using Kinect-style depth cameras has been extensively studied. In this paper, we propose an approach for accurate camera tracking and volumetric dense surface reconstruction, assuming that a known cuboid reference object is present in the scene. Our contribution is threefold. First, we maintain the drift-free camera pose tracking by incorporating the 3D geometric constraints of the cuboid reference object into the image registration process. Second, we reformulate the problem of depth stream fusion as a binary classification problem, enabling high-fidelity surface reconstruction, especially in the concave zones of objects. Third, we further present a surface denoising strategy to mitigate the topological inconsistency (e.g., holes and dangling triangles), which facilitates the generation of a noise-free triangle mesh. We extend our public dataset CU3D with several new image sequences, test our algorithm on these sequences, and quantitatively compare them with other state-of-the-art algorithms. Both our dataset and our algorithm are available as open-source content at https://github.com/zhangxaochen/CuFusion for other researchers to reproduce and verify our results.

Keywords

• 3D object reconstruction
• depth cameras
• Kinect sensors
• open source
• signal denoising
• SLAM