LTGNet: Linear Temporal 3D Object Detection With Geometric-Robust Encoding and Frequency-Domain Smoothing

This paper introduces LTGNet, a robust and efficient framework designed for temporal 3D object detection from long-range LiDAR sequences. Existing sequential detectors face three key challenges: the motion sensitivity of Cartesian representations, the accumulation of noise and misalignment over long horizons, and the quadratic cost of attention-based temporal fusion. LTGNet addresses these issues with three efficient components. A tetrahedral geometric encoder produces rotation- and translation-robust local geometric features to enhance temporal consistency, a frequency-domain Gaussian module suppresses high-frequency noise and minor misalignments across time, and a Mamba-based linear temporal model equipped with a spatiotemporal shift mechanism enables global reasoning with$O(N)$complexity. Experiments on the Waymo Open Dataset demonstrate that LTGNet achieves competitive or superior accuracy while significantly improving efficiency, making it a strong solution for long-term LiDAR-based 3D detection.