Point cloud compression is a pivotal technology for efficient storage and transmission of 3D point cloud data, which has significant implications for practical applications in virtual reality, autonomous driving, and cultural heritage preservation. In this paper, we propose a new learning-based model using the Retentive Network (RetNet) for point cloud compression, which achieves a lower bitrate while maintaining a high peak signal-to-noise ratio (PSNR). We first use an octree structure to segment the point cloud objects. Then, we use octree-based contextual windows to extract pivotal features from relevant sibling and ancestor nodes. Finally, we employ our proposed Octree-Retention model to effectively exploit the prior information between the spatially adjacent nodes for compression. The experimental results show that our method outperforms the state-of-the-art methods on both the LIDAR dataset(SemanticKITTI) and the object dataset(MPEG 8i), demonstrating its effectiveness.

Deficiencies in road anomaly detection systems precipitate multifaceted risks, including elevated collision probabilities from unidentified hazards, compromised traffic flow efficiency, and exponential maintenance costs. Contemporary methods struggle with complex road environments, dynamic viewing perspectives, and limited datasets. We present AVD-YOLO, an enhanced YOLO variant that synergistically integrates Active Vision-Driven (AVD) multi-scale feature extraction with Position Modulated Attention (PMA) mechanisms. PMA addresses diminished target-background discriminability under variable illumination and weather conditions by capturing long range spatial dependencies, enhancing weak-feature target detection. The AVD technique mitigates missed detections caused by real-time viewing distance variations through adaptive multi-receptive field mechanisms, maintaining conceptual target fixation while dynamically adjusting feature scales. To address data scarcity, a comprehensive Multi-Class Road Anomaly Dataset (MCRAD) comprising 14,208 annotated images across nine anomaly categories is constructed. Experiments demonstrate that AVD-YOLO improves detection accuracy, achieving a 1.6% gain in mAP@0.5 and a 2.9% improvement in F1-score over baseline. These performance gains indicate both more precise localization of abnormal objects and a better balance between precision and recall, thereby enhancing the overall robustness of the detection model.