English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Zendy Plus

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Zendy Tools

Zendy Open

Estimating accurate 3D locations of objects from monocular images is achallenging problem because of lacking depth. Previous work shows thatutilizing the object's keypoint projection constraints to estimate multipledepth candidates boosts the detection performance. However, the existingmethods can only utilize vertical edges as projection constraints for depthestimation. So these methods only use a small number of projection constraintsand produce insufficient depth candidates, leading to inaccurate depthestimation. In this paper, we propose a method that utilizes dense projectionconstraints from edges of any direction. In this way, we employ much moreprojection constraints and produce considerable depth candidates. Besides, wepresent a graph matching weighting module to merge the depth candidates. Theproposed method DCD (Densely Constrained Detector) achieves state-of-the-artperformance on the KITTI and WOD benchmarks. Code is released athttps://github.com/BraveGroup/DCD.

Densely Constrained Depth Estimator for Monocular 3D Object Detection