Search Results for author:
Found 13 papers, 8 papers with code
Learning Audio-Driven Viseme Dynamics for 3D Face Animation
We show that the predicted viseme curves can be applied to different viseme-rigged characters to yield various personalized animations with realistic and natural facial motions.
MC-Nonlocal-PINNs: handling nonlocal operators in PINNs via Monte Carlo sampling
We propose, Monte Carlo Nonlocal physics-informed neural networks (MC-Nonlocal-PINNs), which is a generalization of MC-fPINNs in \cite{guo2022monte}, for solving general nonlocal models such as integral equations and nonlocal PDEs.
Flattening-Net: Deep Regular 2D Representation for 3D Point Cloud Analysis
In this paper, we present an unsupervised deep neural architecture called Flattening-Net to represent irregular 3D point clouds of arbitrary geometry and topology as a completely regular 2D point geometry image (PGI) structure, in which coordinates of spatial points are captured in colors of image pixels.
PointMCD: Boosting Deep Point Cloud Encoders via Multi-view Cross-modal Distillation for 3D Shape Recognition
In this paper, we explore the possibility of boosting deep 3D point cloud encoders by transferring visual knowledge extracted from deep 2D image encoders under a standard teacher-student distillation workflow.
Semi-signed prioritized neural fitting for surface reconstruction from unoriented point clouds
To guide the network quickly fit the coarse shape, we propose to utilize the signed supervision in regions that are obviously outside the object and can be easily determined, resulting in our semi-signed supervision.
WarpingGAN: Warping Multiple Uniform Priors for Adversarial 3D Point Cloud Generation
We propose WarpingGAN, an effective and efficient 3D point cloud generation network.
IDEA-Net: Dynamic 3D Point Cloud Interpolation via Deep Embedding Alignment
This paper investigates the problem of temporally interpolating dynamic 3D point clouds with large non-rigid deformation.
CorrNet3D: Unsupervised End-to-end Learning of Dense Correspondence for 3D Point Clouds
The symmetric deformer, with an additional regularized loss, transforms the two permuted point clouds to each other to drive the unsupervised learning of the correspondence.
Ranked #6 on
3D Dense Shape Correspondence
on SHREC'19
(using extra training data)
ParaNet: Deep Regular Representation for 3D Point Clouds
Although convolutional neural networks have achieved remarkable success in analyzing 2D images/videos, it is still non-trivial to apply the well-developed 2D techniques in regular domains to the irregular 3D point cloud data.
Deep Magnification-Flexible Upsampling over 3D Point Clouds
In addition, we propose a simple yet effective training strategy to drive such a flexible ability.
MOPS-Net: A Matrix Optimization-driven Network forTask-Oriented 3D Point Cloud Downsampling
This paper explores the problem of task-oriented downsampling over 3D point clouds, which aims to downsample a point cloud while maintaining the performance of subsequent applications applied to the downsampled sparse points as much as possible.
PUGeo-Net: A Geometry-centric Network for 3D Point Cloud Upsampling
Matrix $\mathbf T$ approximates the augmented Jacobian matrix of a local parameterization and builds a one-to-one correspondence between the 2D parametric domain and the 3D tangent plane so that we can lift the adaptively distributed 2D samples (which are also learned from data) to 3D space.
Nested Network with Two-Stream Pyramid for Salient Object Detection in Optical Remote Sensing Images
Arising from the various object types and scales, diverse imaging orientations, and cluttered backgrounds in optical remote sensing image (RSI), it is difficult to directly extend the success of salient object detection for nature scene image to the optical RSI.
