Papers › Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks

Occlusion-Net: 2D/3D Occluded Keypoint Localization Using Graph Networks

1 Jun 2019CVPR 2019 6archive 2025-07-28

N. Dinesh Reddy, Minh Vo, Srinivasa G. Narasimhan

We present Occlusion-Net, a framework to predict 2D and 3D locations of occluded keypoints for objects, in a largely self-supervised manner. We use an off-the-shelf detector as input (like MaskRCNN) that is trained only on visible key point annotations. This is the only supervision used in this work. A graph encoder network then explicitly classifies invisible edges and a graph decoder network corrects the occluded keypoint locations from the initial detector. Central to this work is a trifocal tensor loss that provides indirect self-supervision for occluded keypoint locations that are visible in other views of the object. The 2D keypoints are then passed into a 3D graph network that estimates the 3D shape and camera pose using the self-supervised re-projection loss. At test time, our approach successfully localizes keypoints in a single view under a diverse set of severe occlusion settings. We demonstrate and evaluate our approach on synthetic CAD data as well as a large image set capturing vehicles at many busy city intersections. As an interesting aside, we compare the accuracy of human labels of invisible keypoints against those obtained from geometric trifocal-tensor loss.

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Tasks

3D Car Instance Understanding3D Object Reconstruction From A Single Image3D Pose EstimationDecoderPose EstimationVehicle Pose Estimation

Results from the paper archive 2025-07-28

TaskDatasetModelMetricValueRank at snapshotLeaderboardReport
3D Pose Estimation CarFusion Occlusion-NET 3DPCK 93.2 #1 of 1 Archive leaderboard report
Vehicle Pose Estimation CarFusion Occlusion-NET PCK 88.8 #1 of 1 Archive leaderboard report

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