Beyond Geometric Augmentation: Hybrid Equivariant Lidar Detection

LiDAR detectors rely heavily on geometric augmentation to learn how object states change with scene geometry. We argue that the burden of geometric ro- bustness should not fall on augmentation alone: geometric structure should also be built into the detector. We investigate this principle through HybridFusion, which couples a steerable equivariant pathway with a non-equivariant stream. Field-wise gate conditioning connects the streams throughout the network, while cross-stream attention and residual merging combine their features in the detec- tion head. On nuScenes, a detailed TransFusion-L study compares standard, pre- dominantly equivariant, and hybrid detectors with and without global geometric augmentation, with balanced resampling disabled. HybridFusion improves mAP by 6.55 percentage points without global augmentation and by 4.83 points with augmented training, with large reductions in orientation and velocity errors. Aug- mentation remains valuable: it adds a further 9.27 mAP points to the hybrid model. Improvements in mAP and NDS also extend to augmented CenterPoint and SEC- OND. A non-equivariant two-stream control fails to recover the hybrid gains, and removing gate conditioning lowers both scores. These detection results support sharing the burden between architecture and augmentation. Hybrid equivariance builds transformation structure into feature processing, improving prediction be- yond what the augmented standard detector achieves while retaining the benefits of augmented training.