[IEEE2019/PaperSummary] LDLS: 3D Object Segmentation through Label
Diffusion from 2D Images

Fig. 1. The full segmentation pipeline, from the input point cloud and image
to the final lidar point cloud segmentation
Eq1: Graph martix of 2D-3D
Eq2: Graph matrix for 3D-3D
Eq3: Label diffusion graph matrix
Eq4:Instance Label vector
Eq5: Iterative diffusion Label
Eq6: Likelihood after convergence
Eq7: Outlier removal equation
Fig2: LDLS algorithm
Table1 : COMPARISON OF SEMANTIC SEGMENTATION ACCURACY
Table2 :INSTANCE SEGMENTATION PERFORMANCE ON MANUAL ANNOTATIONS.
Fig3:Effect of range on semantic and instance segmentation precision and
recall. Instance segmentation metrics use IoU = :70.
Fig4:Scatter plot showing object range versus segmentation IoU. Each point
is a pedestrian or car instance. Zero IoU points indicate false negatives
Table3: Ablation Study
Fig. 5. Qualitative results from running LDLS on the KITTI Drive 91 sequences, as well as on data collected on the Cornell campus using a mobile robot

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