PLOT: Pseudo-Labeling via Object Tracking for Monocular 3D Object Detection

Monocular 3D object detection is crucial for scalable per-ception across fields like autonomous driving, robotics, and surveillance.However, progress is hindered by limited 3D annotations and the in-herent ambiguity of single-image geometry. Existing methods often relyon strong geometric assumptions or carefully curated datasets, whichlimit their applicability to real-world scenarios. In this paper, we presentPLOT (Pseudo-Labeling via Object Tracking), a framework that gen-erates 3D annotations from monocular videos without auxiliary sen-sors or model retraining. PLOT tracks object and background trajecto-ries to estimate camera motion and perform object association in pose-unknown settings. These trajectories provide point correspondences thatalign frame-wise pseudo-LiDARs, which are then fused via simple opti-mization into a unified object shape robust to occlusion and viewpointshifts. Recognizing temporal coherence as a fundamental requirementfor reliable shape fusion and video perception, we design a global ob-ject memory that preserves consistent object identities across frames.PLOT achieves robust annotation quality and strong generalization onboth M3OD video benchmarks and in-the-wild videos, proving its effec-tiveness across diverse and unconstrained domains. Project page: https://plot-eccv.github.io.