Reconstructing 3D structures from non-overlapping multi-view images is a crucial task in the field of 3D computer vision, since it is difficult to establish feature correspondences and infer depth from overlapping parts of views. Previous methods, whether generating the surface mesh or volume of an object, face challenges in simultaneously ensuring the accuracy of detailed topology and the integrity of the overall structure. In this paper, we introduce a novel coarse-to-fine Transformer-based reconstruction network to generate precise point clouds from multiple input images at sparse and non-overlapping viewpoints. Specifically, we firstly employ a general point cloud generation architecture enhanced by the concept of adaptive centroid constraint for the coarse point cloud corresponding to the object. Subsequently, a Transformer-based refinement module applies deformation to each point. We design an attention-based encoder to encode both image projection features and point cloud geometric features, along with a decoder to calculate deformation residuals. Experiments on ShapeNet demonstrate that our proposed method outperforms other competing methods.

Rock-mass point-cloud registration is a critical yet challenging task in the fields of geology and engineering. Currently, the lack of dedicated datasets for rock-mass point-cloud registration significantly limits the development and application of advanced algorithms in this area. To address this gap, we introduce RockCloud-Align, a large-scale dataset specifically designed for rock-mass point-cloud registration. Created using high-resolution LiDAR scans, this dataset covers a wide range of geological scenarios with varying densities and includes over 14,000 meticulously curated point-cloud pairs. RockCloud-Align provides a comprehensive benchmark for evaluating registration algorithms, along with a robust evaluation protocol to standardize the assessment of these methods. Building upon this dataset, we propose a novel registration method that eliminates the dependence on feature points and random sampling consensus, ensuring high efficiency and precision across diverse scenes and densities. Extensive experiments demonstrate that the proposed method significantly outperforms existing approaches in both accuracy and computational efficiency.