Abstract
Point cloud data, widely utilized in diverse applications such as autonomous navigation, heritage preservation, and geospatial modeling, frequently suffers from incompleteness due to occlusions, reflective surfaces, and limited sensor viewpoints. This incompleteness significantly impacts downstream tasks like classification, segmentation, and accurate surface reconstruction. To address this challenge, this paper presents a method for filling missing regions in point cloud data using cubic Bézier curves explicitly constrained by c1 continuity. The proposed technique identifies principal directions of data variance to partition the incomplete point cloud into manageable segments through a 3D slicing approach, facilitating efficient and localized control-point computation and curve fitting. Experimental evaluation demonstrates the high accuracy and geometric fidelity of the reconstructed surfaces, quantified by minimal Euclidean distance errors compared to ground truth data. These results confirm the robustness and effectiveness of the proposed Bézier-based reconstruction approach, providing smooth, structurally coherent, and visually consistent surfaces