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

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

Blog Article

The 3D campicon.com 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 animed blue lotion topical spray 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.