Search Results for author:
Found 18 papers, 6 papers with code
DPDist: Comparing Point Clouds Using Deep Point Cloud Distance
We introduce a new deep learning method for point cloud comparison.
From Compass and Ruler to Convolution and Nonlinearity: On the Surprising Difficulty of Understanding a Simple CNN Solving a Simple Geometric Estimation Task
Neural networks are omnipresent, but remain poorly understood.
Assessing hierarchies by their consistent segmentations
We found that the obtainable segmentation quality varies significantly depending on the way that the segments are specified by the hierarchy elements, and that representing a segmentation with only a few hierarchy elements is often possible.
On the Minimal Recognizable Image Patch
In contrast to human vision, common recognition algorithms often fail on partially occluded images.
DPDist : Comparing Point Clouds Using Deep Point Cloud Distance
We introduce a new deep learning method for point cloud comparison.
Enhancing Generic Segmentation with Learned Region Representations
This representation is combined with an edge map to yield a new segmentation algorithm.
Learning Pixel Representations for Generic Segmentation
Our main contribution is a new method for learning a pixel-wise representation that reflects segment relatedness.
Seeing Things in Random-Dot Videos
Humans possess an intricate and powerful visual system in order to perceive and understand the environing world.
Nesti-Net: Normal Estimation for Unstructured 3D Point Clouds using Convolutional Neural Networks
In this paper, we propose a normal estimation method for unstructured 3D point clouds.
Ranked #8 on
Surface Normals Estimation
on PCPNet
3D Point Cloud Classification and Segmentation using 3D Modified Fisher Vector Representation for Convolutional Neural Networks
The point cloud is gaining prominence as a method for representing 3D shapes, but its irregular format poses a challenge for deep learning methods.
Ranked #57 on
3D Part Segmentation
on ShapeNet-Part
Increasing CNN Robustness to Occlusions by Reducing Filter Support
A straightforward way to improve classification under occlusion conditions is to train the classifier using partially occluded object examples.
Inner-Scene Similarities as a Contextual Cue for Object Detection
In this work, we explore a different kind of contextual information: inner-scene similarity.
Graph Based Over-Segmentation Methods for 3D Point Clouds
This 3D information introduces a new conceptual change that can be utilized to improve the results of over-segmentation, which uses mainly color information, and to generate clusters of points we call super-points.
Local Variation as a Statistical Hypothesis Test
The goal of image oversegmentation is to divide an image into several pieces, each of which should ideally be part of an object.
Approximating Hierarchical MV-sets for Hierarchical Clustering
The goal of hierarchical clustering is to construct a cluster tree, which can be viewed as the modal structure of a density.
q-OCSVM: A q-Quantile Estimator for High-Dimensional Distributions
In this paper we introduce a novel method that can efficiently estimate a family of hierarchical dense sets in high-dimensional distributions.
Learning High-Density Regions for a Generalized Kolmogorov-Smirnov Test in High-Dimensional Data
We propose an efficient, generalized, nonparametric, statistical Kolmogorov-Smirnov test for detecting distributional change in high-dimensional data.
The farthest point strategy for progressive image sampling
A new method of "farthest point strategy" (FPS) for progressive image acquisition is presented.
