A New Volumetric Fusion Strategy with Adaptive Weight Field for RGB-D Reconstruction

High-quality 3D reconstruction results are very important in many application fields. However, current texture generation methods based on point sampling and fusion often produce blur. To solve this problem, we propose a new volumetric fusion strategy which can be embedded in the current online and offline reconstruction framework as a basic module to achieve excellent geometry and texture effects. The improvement comes from two aspects. Firstly, we establish an adaptive weight field to evaluate and adjust the reliability of data from RGB-D images by using a probabilistic and heuristic method. By using this adaptive weight field to guide the voxel fusion process, we can effectively preserve the local texture structure of the mesh, avoid wrong texture problems and suppress the influence of outlier noise on the geometric surface. Secondly, we use a new texture fusion strategy that combines replacement, integration, and fixedness operations to fuse and update voxel texture to reduce blur. Experimental results demonstrate that compared with the classical KinectFusion, our approach can significantly improve the accuracy in geometry and texture clarity, and can achieve equivalent texture reconstruction effects in real-time as the offline reconstruction methods such as intrinsic3d, even better in relief scenes

Influence of Copper Powder Morphology on the Microstructure and Properties of Copper Matrix Bulk Composites Reinforced with Ni-doped Graphene

To clarify the effect of copper powder morphology on the microstructure and properties of copper matrix bulk composites reinforced with Ni-doped graphene, spherical and dendritic copper powders were selected to fabricate the Ni-doped graphene reinforced copper matrix bulk composites. The Ni-doped graphene were synthesized by hydrothermal reduction method, followed by mixing with copper powders, and then consolidated by spark plasma sintering. It is found that the Ni-doped graphene are well bonded with the dendritic copper powder, whereas Ni-doped graphene are relatively independent on the spherical copper powder. The copper base bulk composite prepared by the dendritic copper powder has better properties than that prepared by spherical copper powder. At 0.5wt.% Ni-doped graphene, the dendritic copper base bulk composite has a good combination of hardness, electrical conductivity and yield strength, which are 81.62 HV, 87.93% IACS and 164 MPa, respectively