Search Results for author:
Found 17 papers, 3 papers with code
Non-Rigid Point Set Registration Networks
In contrast, the proposed point registration neural network (PR-Net) actively learns the registration pattern as a parametric function from a training dataset, consequently predict the desired geometric transformation to align a pair of point sets.
Coherent Point Drift Networks: Unsupervised Learning of Non-Rigid Point Set Registration
In contrast to previous efforts (e. g. coherent point drift), CPD-Net can learn displacement field function to estimate geometric transformation from a training dataset, consequently, to predict the desired geometric transformation for the alignment of previously unseen pairs without any additional iterative optimization process.
Arbicon-Net: Arbitrary Continuous Geometric Transformation Networks for Image Registration
To address this issue, we present an end-to-end trainable deep neural networks, named Arbitrary Continuous Geometric Transformation Networks (Arbicon-Net), to directly predict the dense displacement field for pairwise image alignment.
Unsupervised 3D Reconstruction from a Single Image via Adversarial Learning
Recent advancements in deep learning opened new opportunities for learning a high-quality 3D model from a single 2D image given sufficient training on large-scale data sets.
Density-Aware Convolutional Networks with Context Encoding for Airborne LiDAR Point Cloud Classification
Based on this convolution module, we further developed a multi-scale fully convolutional neural network with downsampling and upsampling blocks to enable hierarchical point feature learning.
Few-Shot Learning of Part-Specific Probability Space for 3D Shape Segmentation
In comparison, we propose a novel 3D shape segmentation method that requires few labeled data for training.
Geometry-Aware Segmentation of Remote Sensing Images via Implicit Height Estimation
Recent studies have shown the benefits of using additional elevation data (e. g., DSM) for enhancing the performance of the semantic segmentation of aerial images.
Unsupervised Learning of 3D Point Set Registration
Moreover, for a pair of source and target point sets, existing deep learning mechanisms require explicitly designed encoders to extract both deep spatial features from unstructured point clouds and their spatial correlation representation, which is further fed to a decoder to regress the desired geometric transformation for point set alignment.
Unsupervised Learning of Global Registration of Temporal Sequence of Point Clouds
Global registration of point clouds aims to find an optimal alignment of a sequence of 2D or 3D point sets.
GP-Aligner: Unsupervised Non-rigid Groupwise Point Set Registration Based On Optimized Group Latent Descriptor
More specifically, for a given group we first define an optimizable Group Latent Descriptor (GLD) to characterize the gruopwise relationship among a group of point sets.
Robust Image Matching By Dynamic Feature Selection
Recent works introduce convolutional neural networks (CNNs) to extract high-level feature maps and find correspondences through feature matching.
Unsupervised Partial Point Set Registration via Joint Shape Completion and Registration
To bridge the performance gaps between partial point set registration with full point set registration, we proposed to incorporate a shape completion network to benefit the registration process.
Deep-3DAligner: Unsupervised 3D Point Set Registration Network With Optimizable Latent Vector
Point cloud registration is the process of aligning a pair of point sets via searching for a geometric transformation.
3D Meta Point Signature: Learning to Learn 3D Point Signature for 3D Dense Shape Correspondence
In this paper, we propose a novel meta-learning based 3D point signature model, named 3Dmetapointsignature (MEPS) network, that is capable of learning robust point signatures in 3D shapes.
3D Meta-Registration: Learning to Learn Registration of 3D Point Clouds
In this paper, we propose a meta-learning based 3D registration model, named 3D Meta-Registration, that is capable of rapidly adapting and well generalizing to new 3D registration tasks for unseen 3D point clouds.
Residual Switching Network for Portfolio Optimization
This paper studies deep learning methodologies for portfolio optimization in the US equities market.
Learn to Learn Metric Space for Few-Shot Segmentation of 3D Shapes
To achieve this, we treat the shape segmentation as a point labeling problem in the metric space.
