  Monocular 3D object detection poses a significant challenge due to the lack
of depth information in RGB images. Many existing methods strive to enhance the
object depth estimation performance by allocating additional parameters for
object depth estimation, utilizing extra modules or data. In contrast, we
introduce a novel metric learning scheme that encourages the model to extract
depth-discriminative features regardless of the visual attributes without
increasing inference time and model size. Our method employs the
distance-preserving function to organize the feature space manifold in relation
to ground-truth object depth. The proposed (K, B, eps)-quasi-isometric loss
leverages predetermined pairwise distance restriction as guidance for adjusting
the distance among object descriptors without disrupting the non-linearity of
the natural feature manifold. Moreover, we introduce an auxiliary head for
object-wise depth estimation, which enhances depth quality while maintaining
the inference time. The broad applicability of our method is demonstrated
through experiments that show improvements in overall performance when
integrated into various baselines. The results show that our method
consistently improves the performance of various baselines by 23.51% and 5.78%
on average across KITTI and Waymo, respectively.
