Recovering point clouds involves the sequential process of sampling and restoration, yet existing methods struggle to effectively leverage both topological and geometric attributes. To address this, we propose an end-to-end architecture named TopGeoFormer, which maintains these critical properties throughout the sampling and restoration phases. First, we revisit traditional feature extraction techniques to yield topological embedding using a continuous mapping of relative relationships between neighboring points, and integrate it in both phases for preserving the structure of the original space. Second, we propose the InterTwining Attention to fully merge topological and geometric embeddings, which queries shape with local awareness in both phases to form a learnable 3D shape context facilitated with point-wise, point-shape-wise, and intra-shape features. Third, we introduce a full geometry loss and a topological constraint loss to optimize the embeddings in both Euclidean and topological spaces. The geometry loss uses inconsistent matching between coarse-to-fine generations and targets for reconstructing better geometric details, and the constraint loss limits embedding variances for better approximation of the topological space. In experiments, we comprehensively analyze the circumstances using the conventional and learning-based sampling/upsampling/recovery algorithms. The quantitative and qualitative results demonstrate that our method significantly outperforms existing sampling and recovery methods.

As a convention, satellites and drones are equipped with sensors of both the visible light spectrum and the infrared (IR) spectrum. However, existing remote sensing object detection methods mostly use RGB images captured by the visible light camera while ignoring IR images. Even for algorithms that take RGB-IR image pairs as input, they may fail to extract all potential features in both spectrums. This letter proposes Multispectral DETR, a remote sensing object detector based on the deformable attention mechanism. To enhance multispectral feature extraction and attention, DropSpectrum and SwitchSpectrum methods are further proposed. DropSpectrum facilitates the extraction of multispectral features by requiring the model to detection some of the targets with only one spectrum. SwitchSpectrum eliminates the level bias caused by the fixed order of RGB-IR feature maps and enhances attention on multispectral features. Experiments on the VEDAI dataset show the state-of-the-art performance of Multispectral DETR and the effectiveness of both DropSpectrum and SwitchSpectrum.