1,274 research outputs found
Deep Learning for Semantic Part Segmentation with High-Level Guidance
In this work we address the task of segmenting an object into its parts, or
semantic part segmentation. We start by adapting a state-of-the-art semantic
segmentation system to this task, and show that a combination of a
fully-convolutional Deep CNN system coupled with Dense CRF labelling provides
excellent results for a broad range of object categories. Still, this approach
remains agnostic to high-level constraints between object parts. We introduce
such prior information by means of the Restricted Boltzmann Machine, adapted to
our task and train our model in an discriminative fashion, as a hidden CRF,
demonstrating that prior information can yield additional improvements. We also
investigate the performance of our approach ``in the wild'', without
information concerning the objects' bounding boxes, using an object detector to
guide a multi-scale segmentation scheme. We evaluate the performance of our
approach on the Penn-Fudan and LFW datasets for the tasks of pedestrian parsing
and face labelling respectively. We show superior performance with respect to
competitive methods that have been extensively engineered on these benchmarks,
as well as realistic qualitative results on part segmentation, even for
occluded or deformable objects. We also provide quantitative and extensive
qualitative results on three classes from the PASCAL Parts dataset. Finally, we
show that our multi-scale segmentation scheme can boost accuracy, recovering
segmentations for finer parts.Comment: 11 pages (including references), 3 figures, 2 table
Distant Vehicle Detection Using Radar and Vision
For autonomous vehicles to be able to operate successfully they need to be
aware of other vehicles with sufficient time to make safe, stable plans. Given
the possible closing speeds between two vehicles, this necessitates the ability
to accurately detect distant vehicles. Many current image-based object
detectors using convolutional neural networks exhibit excellent performance on
existing datasets such as KITTI. However, the performance of these networks
falls when detecting small (distant) objects. We demonstrate that incorporating
radar data can boost performance in these difficult situations. We also
introduce an efficient automated method for training data generation using
cameras of different focal lengths
Frustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and
outdoor scenes. While previous methods focus on images or 3D voxels, often
obscuring natural 3D patterns and invariances of 3D data, we directly operate
on raw point clouds by popping up RGB-D scans. However, a key challenge of this
approach is how to efficiently localize objects in point clouds of large-scale
scenes (region proposal). Instead of solely relying on 3D proposals, our method
leverages both mature 2D object detectors and advanced 3D deep learning for
object localization, achieving efficiency as well as high recall for even small
objects. Benefited from learning directly in raw point clouds, our method is
also able to precisely estimate 3D bounding boxes even under strong occlusion
or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection
benchmarks, our method outperforms the state of the art by remarkable margins
while having real-time capability.Comment: 15 pages, 12 figures, 14 table
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