Xiongwu Xiao

Yiwei Xu, Xiang Wang, Yifei Yu et al.

True Digital Orthophoto Map (TDOM) serves as a crucial geospatial product in various fields such as urban management, city planning, land surveying, etc. However, traditional TDOM generation methods generally rely on a complex offline photogrammetric pipeline, resulting in delays that hinder real-time applications. Moreover, the quality of TDOM may degrade due to various challenges, such as inaccurate camera poses or Digital Surface Model (DSM) and scene occlusions. To address these challenges, this work introduces A-TDOM, a near real-time TDOM generation method based on On-the-Fly 3DGS optimization. As each image is acquired, its pose and sparse point cloud are computed via On-the-Fly SfM. Then new Gaussians are integrated and optimized into previously unseen or coarsely reconstructed regions. By integrating with orthogonal splatting, A-TDOM can render just after each update of a new 3DGS field. Initial experiments on multiple benchmarks show that the proposed A-TDOM is capable of actively rendering TDOM in near real-time, with 3DGS optimization for each new image in seconds while maintaining acceptable rendering quality and TDOM geometric accuracy.

Zhen Cao, Wenxiao Zhang, Xin Wen et al.

Unpaired 3D object completion aims to predict a complete 3D shape from an incomplete input without knowing the correspondence between the complete and incomplete shapes. In this paper, we propose the novel KTNet to solve this task from the new perspective of knowledge transfer. KTNet elaborates a teacher-assistant-student network to establish multiple knowledge transfer processes. Specifically, the teacher network takes complete shape as input and learns the knowledge of complete shape. The student network takes the incomplete one as input and restores the corresponding complete shape. And the assistant modules not only help to transfer the knowledge of complete shape from the teacher to the student, but also judge the learning effect of the student network. As a result, KTNet makes use of a more comprehensive understanding to establish the geometric correspondence between complete and incomplete shapes in a perspective of knowledge transfer, which enables more detailed geometric inference for generating high-quality complete shapes. We conduct comprehensive experiments on several datasets, and the results show that our method outperforms previous methods of unpaired point cloud completion by a large margin.