MessyTable: Instance Association in Multiple Camera Views
This dataset addresses instance association problems in real-world scenarios for computer vision researchers, though it is incremental as it focuses on creating a new benchmark rather than proposing a novel method.
The authors introduced the MessyTable dataset, which features complex scenes of messy tables captured from multiple camera views, challenging existing methods in instance association due to identical, stacked, and occluded objects, and reported findings that popular baselines often fail on this task.
We present an interesting and challenging dataset that features a large number of scenes with messy tables captured from multiple camera views. Each scene in this dataset is highly complex, containing multiple object instances that could be identical, stacked and occluded by other instances. The key challenge is to associate all instances given the RGB image of all views. The seemingly simple task surprisingly fails many popular methods or heuristics that we assume good performance in object association. The dataset challenges existing methods in mining subtle appearance differences, reasoning based on contexts, and fusing appearance with geometric cues for establishing an association. We report interesting findings with some popular baselines, and discuss how this dataset could help inspire new problems and catalyse more robust formulations to tackle real-world instance association problems. Project page: $\href{https://caizhongang.github.io/projects/MessyTable/}{\text{MessyTable}}$