Panoramic depth estimation is fundamental to comprehensive 3D scene understanding and serves as a core component of many panoramic vision tasks. However, panoramic images are typically represented in 2D equirectangular projection, which introduces severe spatial distortions from the equator to the poles, posing a central challenge for accurate depth estimation. Existing methods address this issue but fail to fully exploit the intrinsic geometric priors of panoramic imagery. In this paper, we propose a Geometry-Prior-Guided Adaptive Distortion Rectification Network (GADRNet) for panoramic depth estimation. By explicitly modeling panoramic geometric priors and introducing an adaptive receptive-field selection mechanism, GADRNet effectively improves monocular panoramic depth estimation. Specifically, we introduce a Distortion-aware Weight Map that adaptively modulates feature responses according to regional distortion levels, guiding the model to emphasize less-distorted equatorial regions. Moreover, we propose an Adaptive Geometric Distortion Rectification Module that injects geometric priors into dual-scale deformable convolutions, enabling adaptive receptive-field selection to address spatially varying distortions and extract multi-scale features. To further enhance representation learning, we design a Global-Local Scene Understanding Module to jointly capture global context and fine-grained local details. In addition, a knowledge distillation strategy is incorporated to further improve performance and generalization. Extensive experiments on two public real-world panoramic benchmarks demonstrate that GADRNet outperforms existing methods and achieves superior performance.

Ensemble clustering has emerged as a powerful framework for analyzing heterogeneous and complex data. Despite the abundance of existing schemes, co-association matrix-based methods remain the mainstream approach. However, focusing solely on pairwise correlations falls short of fully capturing the intricate cluster relationships. Moreover, despite its potential, ensemble clustering has yet to effectively leverage the powerful representation capabilities of neural networks. To address these limitations, we propose a deep ensemble clustering method called Ensemble Clustering with Attentional Representation (ECAR). Our method considers the results of base partition as groups with related information to explore higher-order fusion information. ECAR captures the importance of each sample's association with its related group by employing an attentional network, and encodes this information into a low-dimensional representation. The attentional network is trained by jointly optimizing the clustering loss from soft assignments learned from the embeddings and the reconstruction loss from the weighted graph generated from ensemble clustering. During training, the weights of base partitions are adaptively refined to promote diversity and consistency while reducing the impact of low-quality and redundant base partitions. Extensive experimental results on real-world datasets demonstrate the substantial improvement of our method over existing baseline ensemble clustering methods and deep clustering methods.

Compared with traditional synthetic aperture radar (SAR), sparse SAR system can obtain higher swath with reduced pulse repetition frequency (PRF). Terrain observation by progressive scan (TOPS) is a novel and promising mode of wide-swath SAR application. It can achieve the same swath coverage as ScanSAR, but greatly reduces the scalloping. To achieve the high-resolution and wide-swath SAR imaging, this paper introduces a novel mode design framework of sparse TOPS SAR system. Combining the advantages of sparse imaging and TOPS, the proposed mode can obtain wider swath by sparse sampling in azimuth direction without changing other SAR hardware parameters, and simultaneously obtains the high-resolution sparse SAR image. The effect on DTAR from steering angle is also suppressed in this mode.

Gaze tracking is not only an important research direction in computer vision but also an important non-verbal clue in human life. What is important is that the direction of gaze can be used as a reference for judging a person’s intentions. In order to improve the accuracy of predicting gaze direction, a model of 3D gaze tracking based on bidirectional Long Short-Term Memory (LSTM) is proposed in this paper. The backbone network of the model is ResNet and its variants. The output of the model is the angular error of gaze direction. To improve the accuracy of the model prediction, the attention mechanism is adopted in this work. The ablation experiments are conducted on the selected Gaze360, which is a dataset with sufficiently large and diverse data. The angular error of the proposed model decreases from 13.5° to 12.6°.