AI Paper Atlas

Research map / 3D vision / Depth and stereo

PlaneRCNN: 3D Plane Detection and Reconstruction From a Single Image

CVPR 2019 · 3D vision › Depth and stereo · approach: Dense feature matching for correspondence · 251 citations (OpenAlex)

Check your idea against 108,967 papers

AI-extracted summary. Written by AI from the paper's title only (no summary yet). Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Measured effects reported in the paper

What was changedWhereMeasuredEffect
Use PlaneRCNN instead of PlaneNet without fine-tuningPlane reconstruction on NYUv2 · NYUv2, w/o fine-tuningRel: 0.220 -> 0.164Helped
Use PlaneRCNN instead of PlaneNet after fine-tuningPlane reconstruction on NYUv2 · NYUv2, w/ fine-tuningRMSE: 0.397 -> 0.395Helped
Add pixel-wise depth estimation (ablation)PlaneRCNN · ScanNetAP@0.6m: 0.329 -> 0.376Helped
Add anchor-based plane normal regression (ablation)PlaneRCNN · ScanNetAP@0.6m: 0.376 -> 0.378Helped
Add the warping loss module (ablation)PlaneRCNN · ScanNetAP@0.6m: 0.378 -> 0.384Helped
Add the segmentation refinement network (ablation)PlaneRCNN · ScanNetSC: 0.766 -> 0.810Helped
Add the segmentation refinement network (ablation)PlaneRCNN · ScanNetAP@0.6m: 0.384 -> 0.386Helped
Add plane offset supervision (ablation)PlaneRCNN · Plane offset estimationPerformanceNo clear effect
Use a detection-based PlaneRCNN rather than PlaneNetPlane detection and reconstruction · ScanNetPlane recallHelped
Use MWS with ground-truth depth rather than PlaneRCNNPlane detection · ScanNet, small depth-error thresholdsPlane recallMixed

Closest papers by meaning

Writing a paper in this area? scime draft is a free LaTeX editor with AI included, real-time co-authors and a check of your references and venue rules before you submit. It also imports OpenAI Prism projects.

Open scime draft

Get the next scime Dispatch and a monthly field update

Free. We send a confirmation link first; every email has a one-click unsubscribe. Privacy