FC-MonoDETR: A Monocular 3D Object Detection Network Based on Foreground Constraint
Daifeng Xiao, Dongbo Yu, Yunbiao Wang, Jun Xiao, Ying Wang, Lupeng Liu · 2025
Estimating 3D information about objects from a single image is a challenging problem in computer vision due to the lack of multi-view information for depth estimation. The Transformer-based methods propagate the target's 3D center depth within its 2D bounding box to construct object-level depth labels. By treating the 2D box area as a unified entity, these methods can perform sufficient feature sampling and parsing within the aforementioned area. This rough but robust detection strategy effectively avoids the dependence of 3D detection on accurate depth estimation of the target center and a few key points nearby. However, due to the lack of effective foreground constraints, these methods struggle to ensure that sampling points are located inside the target, while exterior points lack valid depth values for supervision, which affects both detection accuracy and stability. To address this issue, we propose a Foreground-Constrained Monocular 3D Object Detector (FC-MonoDETR). First, we leverage 2D annotations to generate target segmentation masks using the Segment Anything Model (SAM), directly establishing depth supervision under foreground constraints. Second, we design an attention-based feature fusion module that utilizes contour information to refine visual features and emphasizes the role of foreground regions in depth estimation, guiding the network to focus more effectively on the foreground during holistic 3D information parsing. Finally, we model the relative depth relationships between targets and optimize the estimation of the target's center depth through a specially designed target center depth loss function. Considering the stability issues of Transformer-based methods, we recommend using a more comprehensive evaluation strategy. The sufficient migration experiments have verified the effectiveness of our constructed foreground-constrained depth supervision and feature fusion module in optimizing Transformer-based methods.