Adverse weather conditions—such as rain, fog, and snow—significantly degrade LiDAR point cloud quality, causing substantial performance deterioration in detection models trained on clean data. To address this, we propose LTDNet, a novel point cloud quality improvement net-work that restores degraded LiDAR scans by learning an end-to-end mapping from corrupted to clean geometry. LTDNet leverages position encoding, spatial–frequency joint feature extraction, weather-aware refinement, and probabilistic pruning to effectively recover structural in-tegrity while suppressing weather-induced noise. To fa-cilitate standardized evaluation, we introduce IQA3D, a new benchmark comprising both synthetic and real-world sequences under adverse weather. This dual-design benchmark serves two complementary purposes: synthet-ic sequences provide pixel-wise correspondences between degraded and clean point clouds for quantitatively as-sessing restoration fidelity, while real-world sequences enable evaluation of the practical impact of improvement methods on downstream 3D object detection under au-thentic weather conditions. This makes IQA3D particular-ly suitable for jointly measuring both perceptual quality and task-level robustness of point cloud improvement models. Extensive experiments on IQA3D demonstrate that LTDNet significantly improves detection perfor-mance across various state-of-the-art 3D detectors and three tested weather conditions, making it a practical and effective solution for robust LiDAR-based detection.