Search Results for author:
Found 19 papers, 12 papers with code
SCAMS: Simultaneous Clustering and Model Selection
While clustering has been well studied in the past decade, model selection has drawn less attention.
Simultaneous Video Defogging and Stereo Reconstruction
We present a method to jointly estimate scene depth and recover the clear latent image from a foggy video sequence.
Simultaneous Clustering and Model Selection for Tensor Affinities
Estimating the number of clusters remains a difficult model selection problem.
Pixel2Mesh: Generating 3D Mesh Models from Single RGB Images
We propose an end-to-end deep learning architecture that produces a 3D shape in triangular mesh from a single color image.
Ranked #3 on
3D Object Reconstruction
on Data3D−R2N2
(Avg F1 metric)
Motion Segmentation by Exploiting Complementary Geometric Models
Many real-world sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for motion segmentation would lead to difficulty.
Ranked #1 on
Motion Segmentation
on KT3DMoSeg
Interactive Image Segmentation With Latent Diversity
The first is trained to synthesize a diverse set of plausible segmentations that conform to the user's input.
Ranked #10 on
Interactive Segmentation
on SBD
Combinatorial Optimization with Graph Convolutional Networks and Guided Tree Search
We present a learning-based approach to computing solutions for certain NP-hard problems.
Learning for Multi-Model and Multi-Type Fitting
Multi-model fitting has been extensively studied from the random sampling and clustering perspectives.
Learning for Multi-Type Subspace Clustering
Subspace clustering has been extensively studied from the hypothesis-and-test, algebraic, and spectral clustering based perspectives.
What Do Single-view 3D Reconstruction Networks Learn?
Convolutional networks for single-view object reconstruction have shown impressive performance and have become a popular subject of research.
Ranked #1 on
3D Reconstruction
on 300W
Pixel2Mesh++: Multi-View 3D Mesh Generation via Deformation
We study the problem of shape generation in 3D mesh representation from a few color images with known camera poses.
3D Rigid Motion Segmentation with Mixed and Unknown Number of Models
Many real-world video sequences cannot be conveniently categorized as general or degenerate; in such cases, imposing a false dichotomy in using the fundamental matrix or homography model for motion segmentation on video sequences would lead to difficulty.
Deep Stereo using Adaptive Thin Volume Representation with Uncertainty Awareness
In contrast, we propose adaptive thin volumes (ATVs); in an ATV, the depth hypothesis of each plane is spatially varying, which adapts to the uncertainties of previous per-pixel depth predictions.
Ranked #13 on
3D Reconstruction
on DTU
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion.
Neural Point Cloud Rendering via Multi-Plane Projection
The input to the network is the raw point cloud of a scene and the output are image or image sequences from a novel view or along a novel camera trajectory.
DeepSFM: Structure From Motion Via Deep Bundle Adjustment
The explicit constraints on both depth (structure) and pose (motion), when combined with the learning components, bring the merit from both traditional BA and emerging deep learning technology.
Video Depth Estimation by Fusing Flow-to-Depth Proposals
Our flow-to-depth layer is differentiable, and thus we can refine camera poses by maximizing the aggregated confidence in the camera pose refinement module.
Pixel2Mesh++: 3D Mesh Generation and Refinement from Multi-View Images
We study the problem of shape generation in 3D mesh representation from a small number of color images with or without camera poses.
PointPWC-Net: Cost Volume on Point Clouds for (Self-)Supervised Scene Flow Estimation
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, that directly processes 3D point cloud scenes with large motions in a coarse-to-fine fashion.
