PVT: An Implicit Surface Reconstruction Framework via Point Voxel Geometric-Aware Transformer

Chuanmao Fan, Chenxi Zhao, Ye Duan; Proceedings of the Winter Conference on Applications of Computer Vision (WACV), 2025, pp. 3013-3023

3D surface reconstruction from unorganized point clouds is a fundamental task in visual computing with numerous applications in areas such as robotics virtual reality augmented reality and animation. To date many deep learning-based surface reconstruction methods have been proposed demonstrating great performance on many benchmark datasets. Among these neural implicit field learning-based methods have gained popularity for their capability of representing complex structures in a continuous implicit distance field. Existing neural implicit field learning methods either utilize voxelized point cloud then feed them to a deep network or directly take points as input. In this paper we propose an implicit surface reconstruction framework based on point voxel geometric-aware transformer PVT to seamlessly integrate point-based convolution with voxel-based convolution using bidirectional transformers. Experiments show that the proposed PVT framework can better encode local geometry details and provide a significant performance boost over existing state-of-the-art methods.