A Point Set Generation Network for 3D Object Reconstruction from a Single Image

CVPR 2017 Haoqiang FanHao SuLeonidas Guibas

Generation of 3D data by deep neural network has been attracting increasing attention in the research community. The majority of extant works resort to regular representations such as volumetric grids or collection of images; however, these representations obscure the natural invariance of 3D shapes under geometric transformations and also suffer from a number of other issues... (read more)

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Results from the Paper


#3 best model for 3D Reconstruction on Data3D−R2N2 (using extra training data)

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TASK DATASET MODEL METRIC NAME METRIC VALUE GLOBAL RANK USES EXTRA
TRAINING DATA
RESULT LEADERBOARD
3D Reconstruction Data3D−R2N2 PSGN 3DIoU 0.640 # 3
3D Object Reconstruction Data3D−R2N2 PSG Avg F1 48.58 # 4