LeAD-M3D: Leveraging Asymmetric Distillation for Real-Time Monocular 3D Detection

Real-time monocular 3D object detection remains challeng-ing due to severe depth ambiguity, viewpoint shifts, and the high compu-tational cost of 3D reasoning. Existing approaches either rely on LiDARor geometric priors to compensate for missing depth or sacrifice efficiencyto achieve competitive accuracy. We introduce LeAD-M3D, a monocu-lar 3D detector that achieves state-of-the-art accuracy and real-time in-ference without extra modalities. Our method is enabled by three keycomponents. Asymmetric Augmentation Denoising Distillation (A2D2)transfers geometric knowledge from a clean-image teacher to a MixUp-noised student via a quality- and importance-weighted depth-feature loss,enabling stronger depth reasoning without LiDAR. 3D-aware ConsistentMatching (CM3D ) improves prediction-to-ground truth assignment by in-tegrating 3D MGIoU into the matching score, yielding stable and precisesupervision. Finally, Confidence-Gated 3D Inference (CGI3D ) acceleratesinference by restricting expensive 3D regression to confident regions. To-gether, these contributions set a new Pareto frontier for monocular 3Ddetection: LeAD-M3D achieves state-of-the-art accuracy on KITTI andWaymo, and the best reported car AP on Rope3D, while running up to3.6 × faster than prior high-accuracy models (e.g., MonoDiff). LeAD-M3D demonstrates that high fidelity and real-time monocular 3D de-tection is simultaneously attainable, without LiDAR, stereo, or stronggeometric assumptions.