Cross-age face retrieval—often formulated as searching a large-scale gallery for the same identity across significant age gaps—remains a fundamental challenge due to the geometric mismatch between static embedding spaces and dynamic biological aging. In real-world retrieval and forensic deployments, preserving the pre-trained identity metric is crucial, yet many existing approaches either synthesize pixels with artifacts or fine-tune backbones, potentially distorting the cosine-based embedding geometry. In this paper, we propose Spherical Projected Bézier Flow (SPBF), a geometry-constrained manifold transport framework that models aging as feature transport on the hypersphere while keeping the backbone frozen. SPBF parameterizes a curvilinear trajectory via a projected Bézier path and learns a tangent velocity field with an Endpoint Consistency Constraint, enabling flexible non-linear and variable-speed dynamics without leaving the unit sphere. By avoiding off-manifold, low-norm states associated with higher uncertainty, SPBF improves retrieval robustness under large age gaps. Extensive experiments on FG-NET, AgeDB, and CACD show that SPBF is competitive with SOTA on homogeneous benchmarks and delivers substantial gains in cross-dataset generalization with only a lightweight plug-in module.

Local sampling plays a key role in modeling 3D point clouds. Due to the disordered and unstructured nature of point cloud data, conventional 3D deep models such as PointNet++ and its variants usually employ random or fixed rules to sample local neighborhoods, leading to considerable redundancy in the feature aggregation process. In this paper, we propose a self-supervised method for learning to adaptively select effective neighbors. Firstly, we observe that only a part of sampled points contributes to the aggregated features after the max-pooling operation in existing point cloud models. Then, based on this observation, we propose a simple and task-oriented metric to evaluate the sampling efficiency by measuring the effective neighbors in the feature aggregation process. The metric is also used to supervise a lightweight neighborhood scoring module (NSM), which is designed to efficiently select effective neighboring points from a wider range of neighbors to reduce the computational cost and keep the performance superior. To further improve the performance, we introduce Neighborhood Attention in the feature aggregation process according to the importance score of neighborhood points predicted by NSM. Experimental results show that our method is simple and efficient, and can be applied to most tasks and models to reduce the computational cost and keep the performance superiority. Our code is available athttps://github.com/sunshuofeng/PointMax_Code

Numerous prior studies predominantly emphasize constructing relation vectors for individual neighborhood points and generating dynamic kernels for each vector and embedding these into high-dimensional spaces to capture implicit local structures. However, we contend that such implicit high-dimensional structure modeling approch inadequately represents the local geometric structure of point clouds due to the absence of explicit structural information. Hence, we introduce X-3D, an explicit 3D structure modeling approach. X-3D functions by capturing the explicit local structural information within the input 3D space and employing it to produce dynamic kernels with shared weights for all neighborhood points within the current local region. This modeling approach introduces effective geometric prior and significantly diminishes the disparity between the local structure of the embedding space and the original input point cloud, thereby improving the extraction of local features. Experiments show that our method can be used on a variety of methods and achieves state-of-the-art performance on segmentation, classification, de-tection tasks with lower extra computational cost, such as 90.7% on ScanObjectNN for classification, 79.2% on S3DIS 6 fold and 74.3% on S3DIS Area 5 for segmentation, 76.3% on ScanNetV2 for segmentation and 64.5% mAP25, 46.9% mAP50on SUN RGB-D and 69.0% mAP25, 51.1% mAP50on ScanNetV2. Our code is available at https://github.com/sunshuofeng/X-3D.