Difficulty-Aware Label-Guided Denoising for Monocular 3D Object Detection

2025-11-17

AI Summary

Monocular 3D detection suffers from depth ambiguity and varying instance difficulty—caused by occlusion, long distance, and truncation—leading to degraded performance. To address this, we propose a difficulty-aware, label-guided denoising framework. First, we introduce an instance-level difficulty estimation mechanism that adaptively applies differentiated label perturbation and reconstruction for samples of varying difficulty, providing explicit geometric supervision. Second, building upon the DETR architecture, we integrate uncertainty-guided label denoising, auxiliary depth prediction, and global attention to enable end-to-end joint optimization of detection and label reconstruction. Our method significantly enhances the model’s capacity to capture complex geometric structures and handle challenging scenarios. On the KITTI benchmark, it achieves state-of-the-art performance, with particularly notable improvements in detecting hard instances.

Technology Category

• Computer Vision: 3D Computer Vision
• Machine Learning: Calibration & Uncertainty Quantification
• Search and Optimization: Learning to Search

Application Category

• Economics, Online Markets and Human Computation: Humans versus LLMs for data annotation and labeling
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Monocular 3D object detection is a cost-effective solution for applications like autonomous driving and robotics, but remains fundamentally ill-posed due to inherently ambiguous depth cues. Recent DETR-based methods attempt to mitigate this through global attention and auxiliary depth prediction, yet they still struggle with inaccurate depth estimates. Moreover, these methods often overlook instance-level detection difficulty, such as occlusion, distance, and truncation, leading to suboptimal detection performance. We propose MonoDLGD, a novel Difficulty-Aware Label-Guided Denoising framework that adaptively perturbs and reconstructs ground-truth labels based on detection uncertainty. Specifically, MonoDLGD applies stronger perturbations to easier instances and weaker ones into harder cases, and then reconstructs them to effectively provide explicit geometric supervision. By jointly optimizing label reconstruction and 3D object detection, MonoDLGD encourages geometry-aware representation learning and improves robustness to varying levels of object complexity. Extensive experiments on the KITTI benchmark demonstrate that MonoDLGD achieves state-of-the-art performance across all difficulty levels.

Problem

• Monocular 3D detection struggles with ambiguous depth estimation
• Existing methods ignore instance-level difficulty like occlusion and distance
• Framework adaptively reconstructs labels using difficulty-aware perturbations

Innovation

• Difficulty-aware label-guided denoising framework
• Adaptive perturbation based on detection uncertainty
• Joint optimization of reconstruction and detection