Objects are Different: Flexible Monocular 3D Object Detection

By Yunpeng Zhang, Jiwen Lu, Jie Zhou. Computer Vision and Pattern Recognition, 2021

338 citations recorded by the source corpus.

The precise localization of 3D objects from a single image without depth information is a highly challenging problem. Most existing methods adopt the same approach for all objects regardless of their diverse distributions, leading to limited performance for truncated objects. In this paper, we propose a flexible framework for monocular 3D object detection which explicitly decouples the truncated objects and adaptively combines multiple approaches for object depth estimation. Specifically, we decouple the edge of the feature map for predicting long-tail truncated objects so that the optimization of normal objects is not influenced. Furthermore, we formulate the object depth estimation as an uncertainty-guided ensemble of directly regressed object depth and solved depths from different groups of keypoints. Experiments demonstrate that our method outperforms the state-of-the-art method by relatively 27% for the moderate level and 30% for the hard level in the test set of KITTI benchmark while maintaining real-time efficiency. Code will be available at https://github.com/zhangyp15/MonoFlex.

Subfields: Aerospace Engineering, Computer Vision and Pattern Recognition

View Paper: https://www.semanticscholar.org/paper/adcd6edd52bd9d42cb38ae3d1019d6dc6cbd96d0

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Citations in the corpus

BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective Supervision (2022, methodology): “Auxiliary losses are ubiquitous in monocular 3D object detection as most methods [15,21,27,31,33,34,41] are built upon 2D detectors like RetinaNet [19] and FCOS [32].”
BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection (2022, background): “In the last few years, KITTI [9] benchmark has fueled the rapid development of monocular 3D object detection [23, 20, 44, 50, 46, 28, 36, 35, 14].”
3D Object Detection for Autonomous Driving: A Survey (2021, methodology): “Keypoint constraints in works [18, 19, 69] are more likely to be vulnerable to slim and tall objects, say, Pedestrians.”
BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework (2022, methodology): “Since there is only one front camera in KITTI, most of the methods have been developed to address monocular 3D detection [29, 40, 28, 19, 63, 66, 39, 49, 48].”
BEVDet: High-performance Multi-camera 3D Object Detection in Bird-Eye-View (2021, background): “In the last few years, KITTI [8] benchmark has fueled the development of monocular 3D object detection [26, 24, 50, 55, 52, 32, 41, 40, 17].”