CVPR 2023 - Neural Part Priors: Learning to Optimize Part-Based Object Completion in

In this episode we discuss Neural Part Priors: Learning to Optimize Part-Based Object Completion in by Alexey Bokhovkin, Angela Dai. The paper proposes learning Neural Part Priors (NPPs) to improve 3D scene understanding. NPPs are parametric spaces of objects and their parts that allow for optimization to fit new input 3D scans while maintaining global scene consistency. The use of coordinate field MLPs facilitates optimization at test time, resulting in more accurate reconstructions and outperforming the state-of-the-art in part decomposition and object completion on the ScanNet dataset. The proposed method improves both object understanding and global scene consistency.