Search Results for author:
Found 8 papers, 1 papers with code
MLGCN: An Ultra Efficient Graph Convolution Neural Model For 3D Point Cloud Analysis
To address these limitations we introduce a novel Multi-level Graph Convolution Neural (MLGCN) model, which uses Graph Neural Networks (GNN) blocks to extract features from 3D point clouds at specific locality levels.
Contour Completion using Deep Structural Priors
We introduce an iterative process that completes an incomplete image and we propose novel measures that guide this to find regions it needs to complete.
RxRx1: A Dataset for Evaluating Experimental Batch Correction Methods
We propose a classification task designed to evaluate the effectiveness of experimental batch correction methods on these images and examine the performance of a number of correction methods on this task.
Average Outward Flux Skeletons for Environment Mapping and Topology Matching
In topology matching between two given maps and their AOF skeletons, we first find correspondences between points on the AOF skeletons of two different environments.
Medial Spectral Coordinates for 3D Shape Analysis
In recent years there has been a resurgence of interest in our community in the shape analysis of 3D objects represented by surface meshes, their voxelized interiors, or surface point clouds.
Contour-guided Image Completion with Perceptual Grouping
Humans are excellent at perceiving illusory outlines.
Appearance Shock Grammar for Fast Medial Axis Extraction from Real Images
We make the following specific contributions: i) we extend the shock graph representation to the domain of real images, by generalizing the shock type definitions using local, appearance-based criteria; ii) we then use the rules of a Shock Grammar to guide our search for medial points, drastically reducing run time when compared to other methods, which exhaustively consider all points in the input image;iii) we remove the need for typical post-processing steps including thinning, non-maximum suppression, and grouping, by adhering to the Shock Grammar rules while deriving the medial axis solution; iv) finally, we raise some fundamental concerns with the evaluation scheme used in previous work and propose a more appropriate alternative for assessing the performance of medial axis extraction from scenes.
Scene Categorization from Contours: Medial Axis Based Salience Measures
That is, the medial axis based salience weights appear to add useful information that is not available when CNNs are trained to use contours alone.
