2.9k citations · 10.7k across the 136 of their papers we have counts for
9 papers · 1 filter
Beyond Holistic Object Recognition: Enriching Image Understanding with Part States
Cewu Lu, Hao Su, Yongyi Lu +3
Important high-level vision tasks such as human-object interaction, image captioning and robotic manipulation require rich semantic descriptions of objects at part level. Based upo…
A Point Set Generation Network for 3D Object Reconstruction from a Single Image
Haoqiang Fan, Hao Su, Leonidas 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…
SyncSpecCNN: Synchronized Spectral CNN for 3D Shape Segmentation
Li Yi, Hao Su, Xingwen Guo +1
In this paper, we study the problem of semantic annotation on 3D models that are represented as shape graphs. A functional view is taken to represent localized information on graph…
PointNet: Deep Learning on Point Sets for 3D Classification and Segmentation
Charles R. Qi, Hao Su, Kaichun Mo +1
Point cloud is an important type of geometric data structure. Due to its irregular format, most researchers transform such data to regular 3D voxel grids or collections of images.…
Learning Shape Abstractions by Assembling Volumetric Primitives
Shubham Tulsiani, Hao Su, Leonidas J. Guibas +2
We present a learning framework for abstracting complex shapes by learning to assemble objects using 3D volumetric primitives. In addition to generating simple and geometrically in…
Distributable Consistent Multi-Object Matching
Nan Hu, Qixing Huang, Boris Thibert +1
In this paper we propose an optimization-based framework to multiple object matching. The framework takes maps computed between pairs of objects as input, and outputs maps that are…