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Found 4 papers, 1 papers with code
WALT: Watch and Learn 2D Amodal Representation From Time-Lapse Imagery
Labeled real data of occlusions is scarce (even in large datasets) and synthetic data leaves a domain gap, making it hard to explicitly model and learn occlusions.
Ranked #1 on
Amodal Instance Segmentation
on WALT
Exploiting & Refining Depth Distributions With Triangulation Light Curtains
Active sensing through the use of Adaptive Depth Sensors is a nascent field, with potential in areas such as Advanced driver-assistance systems (ADAS).
Toward Planet-Wide Traffic Camera Calibration
Despite the widespread deployment of outdoor cameras, their potential for automated analysis remains largely untapped due, in part, to calibration challenges.
WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion
Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments, partly due to the lack of large-scale labeled ground-truth annotations for learning occlusion.
