188,665 research outputs found
NTU RGB+D 120: A Large-Scale Benchmark for 3D Human Activity Understanding
Research on depth-based human activity analysis achieved outstanding
performance and demonstrated the effectiveness of 3D representation for action
recognition. The existing depth-based and RGB+D-based action recognition
benchmarks have a number of limitations, including the lack of large-scale
training samples, realistic number of distinct class categories, diversity in
camera views, varied environmental conditions, and variety of human subjects.
In this work, we introduce a large-scale dataset for RGB+D human action
recognition, which is collected from 106 distinct subjects and contains more
than 114 thousand video samples and 8 million frames. This dataset contains 120
different action classes including daily, mutual, and health-related
activities. We evaluate the performance of a series of existing 3D activity
analysis methods on this dataset, and show the advantage of applying deep
learning methods for 3D-based human action recognition. Furthermore, we
investigate a novel one-shot 3D activity recognition problem on our dataset,
and a simple yet effective Action-Part Semantic Relevance-aware (APSR)
framework is proposed for this task, which yields promising results for
recognition of the novel action classes. We believe the introduction of this
large-scale dataset will enable the community to apply, adapt, and develop
various data-hungry learning techniques for depth-based and RGB+D-based human
activity understanding. [The dataset is available at:
http://rose1.ntu.edu.sg/Datasets/actionRecognition.asp]Comment: IEEE Transactions on Pattern Analysis and Machine Intelligence
(TPAMI
Multi-View 3D Object Detection Network for Autonomous Driving
This paper aims at high-accuracy 3D object detection in autonomous driving
scenario. We propose Multi-View 3D networks (MV3D), a sensory-fusion framework
that takes both LIDAR point cloud and RGB images as input and predicts oriented
3D bounding boxes. We encode the sparse 3D point cloud with a compact
multi-view representation. The network is composed of two subnetworks: one for
3D object proposal generation and another for multi-view feature fusion. The
proposal network generates 3D candidate boxes efficiently from the bird's eye
view representation of 3D point cloud. We design a deep fusion scheme to
combine region-wise features from multiple views and enable interactions
between intermediate layers of different paths. Experiments on the challenging
KITTI benchmark show that our approach outperforms the state-of-the-art by
around 25% and 30% AP on the tasks of 3D localization and 3D detection. In
addition, for 2D detection, our approach obtains 10.3% higher AP than the
state-of-the-art on the hard data among the LIDAR-based methods.Comment: To appear in IEEE Conference on Computer Vision and Pattern
Recognition (CVPR) 201
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