mlsanityData to 14 Oct 2026

Instance-wise Occlusion and Depth Orders in Natural Scenes

arXiv 2111.14562 ↗First posted 29 Nov 2021cs.CV cs.AIcited by 31

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Abstract (arXiv)

In this paper, we introduce a new dataset, named InstaOrder, that can be used to understand the geometrical relationships of instances in an image. The dataset consists of 2.9M annotations of geometric orderings for class-labeled instances in 101K natural scenes. The scenes were annotated by 3,659 crowd-workers regarding (1) occlusion order that identifies occluder/occludee and (2) depth order that describes ordinal relations that consider relative distance from the camera. The dataset provides joint annotation of two kinds of orderings for the same instances, and we discover that the occlusion order and depth order are complementary. We also introduce a geometric order prediction network called InstaOrderNet, which is superior to state-of-the-art approaches. Moreover, we propose a dense depth prediction network called InstaDepthNet that uses auxiliary geometric order loss to boost the accuracy of the state-of-the-art depth prediction approach, MiDaS [56].

Key facts. Cited by 31 papers in the map (data through 14 Oct 2026). Builds on 61 of its 86 references that are in the map. First posted on arXiv 29 Nov 2021.

Builds on

61 of 86 references.

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Citing papers per year: 2021: 1; 2022: 1; 2023: 5; 2024: 6; 2025: 11; 2026: 7.

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