Abstract
Weakly supervised monocular 3D detection, while less annotation-intensive, often struggles to capture the global context required for reliable 3D reasoning. Conventional label-efficient methods focus on object-centric features, neglecting contextual semantic relationships that are critical in complex scenes. In this work, we propose a Context-Aware Weak Supervision for Monocular 3D object detection, namely CA-W3D, to address this limitation in a two-stage training paradigm. Specifically, we first introduce a pre-training stage employing Region-wise Object Contrastive Matching (ROCM), which aligns regional object embeddings derived from a trainable monocular 3D encoder and a frozen open-vocabulary 2D visual grounding model. This alignment encourages the monocular encoder to discriminate scene-specific attributes and acquire richer contextual knowledge. In the second stage, we incorporate a pseudo-label training process with a Dual-to-One Distillation (D2OD) mechanism, which effectively transfers contextual priors into the monocular encoder while preserving spatial fidelity and maintaining computational efficiency during inference. Extensive experiments conducted on the public KITTI benchmark demonstrate the effectiveness of our approach, surpassing the SoTA method over all metrics, highlighting the importance of contextual-aware knowledge in weakly-supervised monocular 3D detection.
Abstract (translated)
弱监督单目3D检测虽然减少了标注需求，但在捕捉可靠3D推理所需的全局上下文方面往往表现不佳。传统的标签高效方法侧重于对象中心特征，忽视了复杂场景中至关重要的语义关系。为此，我们提出了一种针对单目3D物体检测的上下文感知弱监督方法（Context-Aware Weak Supervision for Monocular 3D object detection），简称CA-W3D，在两阶段训练范式下解决这一局限性。 具体来说，我们在预训练阶段引入了基于区域对象对比匹配（Region-wise Object Contrastive Matching, ROCM）的方法。该方法使可调单目3D编码器与固定的开放词汇2D视觉定位模型中提取的区域对象嵌入对齐。这种对齐鼓励单目编码器区分场景特定属性，并获得更丰富的上下文知识。 在第二阶段，我们采用了一种伪标签训练过程，结合了双向到单一蒸馏机制（Dual-to-One Distillation, D2OD），该机制有效将上下文先验传递给单目编码器，同时保持空间精度和推理时的计算效率。 我们在公共KITTI基准上进行了广泛的实验，验证了我们方法的有效性，在所有指标上超越了现有最佳方法。这突显了在弱监督下的单目3D检测中，上下文感知知识的重要性。
URL
https://arxiv.org/abs/2503.04154
