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Semantic Segmentation of Radar Detections using Convolutions on Point Clouds
State-of-the-art algorithms for environment perception based on radar scans build up on deep neural network architectures that can be costly in terms of memory and computation.
Semantic Pose using Deep Networks Trained on Synthetic RGB-D
In this work we address the problem of indoor scene understanding from RGB-D images.
Constrained Planar Cuts - Object Partitioning for Point Clouds
We evaluate our algorithm on recordings from an RGB-D camera as well as the Princeton Segmentation Benchmark, using a fixed set of parameters across all object classes.
Object Partitioning using Local Convexity
As an alternative to this, we present a new, efficient learning- and model-free approach for the segmentation of 3D point clouds into object parts.
Voxel Cloud Connectivity Segmentation - Supervoxels for Point Clouds
Unsupervised over-segmentation of an image into regions of perceptually similar pixels, known as superpixels, is a widely used preprocessing step in segmentation algorithms.
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