LiDAR sensors are critical for 3D scene perception in autonomous driving, providing high spatial accuracy but suffering from limited temporal resolution. Point cloud interpolation, which reconstructs intermediate frames from sparsely captured inputs, effectively enhances temporal continuity. However, the challenges posed by non-linear and non-rigid motion in dynamic scenarios remain unresolved by existing methods, which often sacrifice accuracy for efficiency. To solve the above problems, we propose MSTSINet (Multiscale Transformer and SSM Interpolation Network), a novel framework that combines multiscale forward flow prediction with non-linear trajectory compensation for fast and accurate interpolation. Unlike conventional bidirectional flow-based methods, our forward flow-guided approach reduces inference time while improving robustness to complex motion. MSTSINet integrates multiscale motion modeling through a novel hierarchical feature extraction and semantic-aware cross-frame attention, progressively improving forward flow estimation for accurate non-linear trajectory prediction. To further improve motion fidelity and temporal coherence, we introduce a point-wise compensation mechanism during inference and design a novel multiscale loss function that supervises forward flow learning across varying temporal resolutions. Experiments on autonomous driving benchmarks demonstrate that MSTSINet significantly outperforms existing state-of-the-art methods, achieving up to a 26.39% reduction in Chamfer Distance and substantially improving inference speed. These advantages make it well-suited for real-time deployment in autonomous driving scenarios.

Ensuring reliable object detection in adverse conditions is paramount for safe autonomous driving. While cameras and LiDAR struggle in such scenarios, Frequency Modulated Continuous Wave (FMCW) radar offers superior robustness, but its low azimuth resolution can hinder object distinction. Leveraging temporal information across multiple frames can mitigate this limitation, but the radar's inherent frame rate poses a constraint. This paper proposes a deep learning-based interpolate-then-detect framework for FMCW radar. In this framework, the radar frame sequence is first interpolated to synthesize high-quality intermediate frames, and object detection is subsequently performed on the resulting densified sequence, thereby improving detection accuracy and temporal stability. Specifically, we propose a unified network that employs multiple Gaussian Splatting Efficient Attention (GSEA) modules. Each GSEA module combines 3D Gaussian splatting to provide a probabilistic, motion-aware embedding that captures spatial uncertainty and variability in radar measurements, while split multi-head attention enlarges the effective spatio-temporal receptive field and selectively emphasizes the most informative features across frames. Experimental results demonstrate that the proposed approach achieves up to a 9.6 % improvement in average precision and exhibits enhanced robustness in noisy environments, highlighting its potential for accurate and reliable object detection in challenging driving scenarios.