DC-PCN: Point Cloud Completion Network with Dual-Codebook Guided Quantization
This work solves the problem of improving precision in 3D shape reconstruction from partial point clouds for applications in computer vision and robotics, representing an incremental advancement over existing methods.
The paper tackles the problem of point cloud completion by addressing variability in sampled point clouds from a single 3D object surface, which causes ambiguity and reduces precision, and introduces DC-PCN, a network with dual-codebook guided quantization that achieves state-of-the-art performance on datasets like PCN, ShapeNet_Part, and ShapeNet34.
Point cloud completion aims to reconstruct complete 3D shapes from partial 3D point clouds. With advancements in deep learning techniques, various methods for point cloud completion have been developed. Despite achieving encouraging results, a significant issue remains: these methods often overlook the variability in point clouds sampled from a single 3D object surface. This variability can lead to ambiguity and hinder the achievement of more precise completion results. Therefore, in this study, we introduce a novel point cloud completion network, namely Dual-Codebook Point Completion Network (DC-PCN), following an encder-decoder pipeline. The primary objective of DC-PCN is to formulate a singular representation of sampled point clouds originating from the same 3D surface. DC-PCN introduces a dual-codebook design to quantize point-cloud representations from a multilevel perspective. It consists of an encoder-codebook and a decoder-codebook, designed to capture distinct point cloud patterns at shallow and deep levels. Additionally, to enhance the information flow between these two codebooks, we devise an information exchange mechanism. This approach ensures that crucial features and patterns from both shallow and deep levels are effectively utilized for completion. Extensive experiments on the PCN, ShapeNet\_Part, and ShapeNet34 datasets demonstrate the state-of-the-art performance of our method.