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Sparse Steerable Convolutions: An Efficient Learning of SE(3)-Equivariant Features for Estimation and Tracking of Object Poses in 3D Space
Jiehong Lin, Hongyang Li, Ke Chen +2
As a basic component of SE(3)-equivariant deep feature learning, steerable convolution has recently demonstrated its advantages for 3D semantic analysis. The advantages are, howeve…
Uncertainty-aware Clustering for Unsupervised Domain Adaptive Object Re-identification
Pengfei Wang, Changxing Ding, Wentao Tan +3
Unsupervised Domain Adaptive (UDA) object re-identification (Re-ID) aims at adapting a model trained on a labeled source domain to an unlabeled target domain. State-of-the-art obje…
Geometry-Aware Self-Training for Unsupervised Domain Adaptationon Object Point Clouds
Longkun Zou, Hui Tang, Ke Chen +1
The point cloud representation of an object can have a large geometric variation in view of inconsistent data acquisition procedure, which thus leads to domain discrepancy due to d…
Instance Segmentation in 3D Scenes using Semantic Superpoint Tree Networks
Zhihao Liang, Zhihao Li, Songcen Xu +2
Instance segmentation in 3D scenes is fundamental in many applications of scene understanding. It is yet challenging due to the compound factors of data irregularity and uncertaint…
Content-Aware Convolutional Neural Networks
Yong Guo, Yaofo Chen, Mingkui Tan +3
Convolutional Neural Networks (CNNs) have achieved great success due to the powerful feature learning ability of convolution layers. Specifically, the standard convolution traverse…
Learning and Meshing from Deep Implicit Surface Networks Using an Efficient Implementation of Analytic Marching
Jiabao Lei, Kui Jia, Yi Ma
Reconstruction of object or scene surfaces has tremendous applications in computer vision, computer graphics, and robotics. In this paper, we study a fundamental problem in this co…