Depth completion with large holes based on structure-guided boundary propagation
When collecting depth information using consumer-depth cameras,the collected depth information is often influenced by factors such as equipment,environment,and object material,often leading to missing depth information and holes,limiting the application of depth images in subsequent vision tasks.Existing depth-completion algorithms often struggle to effectively address large-area depth missing,resulting in poor complementation effect and poor object boundary maintenance.To tackle these two problems,a depth-completion algorithm for large holes based on structure-guided boundary growth was proposed.First,combined with the boundary information provided by the RGB images,the structure-guided boundary growth strategy was employed to complement the depth loss at the object boundary.Finally,the large holes inside the object were complemented using a combination of large-hole cut-and-fill and mean filtering.The experimental results demonstrated that the algorithm was able to efficiently maintain object boundaries with large missing areas and across missing objects,while being able to complement the depth information of large missing areas.Quantitative and qualitative results on multiple datasets demonstrated the effectiveness of the method.
NETL
NSTL
万方数据
