Fudan University

Haitao Lin

Yanwei Fu

Xiangyang Xue

Yinda Zhang

Chao Wen

Jiashun Wang

Tianyun Zou

A network for category-level 6D pose and size estimation, including a 3D-OCR module for 3D Orientation-Consistent Representation, a GeoReS module for Geometry-constrained Reflection Symmetry, and a MPDE module for Mirror-Paired Dimensional Estimation; wherein the 3D-OCR module and the GeoReS module are incorporated in parallel; the 3D-OCR module receives a canonical template shape including canonical category-specific keypoints; the GeoReS module receives an original input depth observation including pre-processed predicted category labels and potential masks of the target instances; the MPDE module receives the output from the GeoReS module as well as the original input depth observation; and the network outputs the estimation results based on the output of the MPDE module, the output of the 3D-OCR module, as well as the canonical template shape. Also provided are corresponding systems and methods.

Network and system for pose and size estimation

A network for neural pose transfer includes a pose feature extractor, and a style transfer decoder, wherein the pose feature extractor comprises a plurality of sequential extracting stacks, each extracting stack consists of a first convolution layer and an Instance Norm layer sequential to the first convolution layer. The style transfer decoder comprises a plurality of sequential decoding stacks, a second convolution layer sequential to the plurality of decoding stacks and a tan h layer sequential to the second convolution layer. Each decoding stack consists of a third convolution layer and a SPAdaIn residual block. A source pose mesh is input to the pose feature extractor, and an identity mesh is concatenated with the output of the pose feature extractor and meanwhile fed to each SPAdaIn residual block of the style transfer decoder. A system thereof is also provided.

Network and system for neural pose transfer

Growing community of inventors

Shanghai, China

Haitao Lin