Monocular 3D Object Detection
Overview
Monocular 3D object detection uses images from a single camera to predict 3D object boxes, including distance, size, and orientation—useful when LiDAR or stereo cameras are unavailable. Approaches estimate depth directly, predict projected box corners, lift image features into bird’s-eye-view or 3D representations, and use geometric priors, temporal video cues, or distillation from stronger models. KITTI, Waymo, and nuScenes are common benchmarks, while newer work tests cross-camera transfer, open-vocabulary recognition, and settings such as aerial wildlife detection. A central challenge is that a single image leaves depth and true object size ambiguous, so improved geometric localization must be balanced against robustness to unfamiliar cameras, scenes, and categories.
Latest papers
22 papers1 paper in the last four weeks, against 1 the four weeks before. 0.0% of all new papers.