Semi-supervised 3D Object Detection via Temporal Graph Neural Networks
- URL: http://arxiv.org/abs/2202.00182v1
- Date: Tue, 1 Feb 2022 02:06:54 GMT
- Title: Semi-supervised 3D Object Detection via Temporal Graph Neural Networks
- Authors: Jianren Wang, Haiming Gang, Siddarth Ancha, Yi-Ting Chen, David Held
- Abstract summary: 3D object detection plays an important role in autonomous driving and other robotics applications.
We propose leveraging large amounts of unlabeled point cloud videos by semi-supervised learning of 3D object detectors.
Our method achieves state-of-the-art detection performance on the challenging nuScenes and H3D benchmarks.
- Score: 17.90796183565084
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: 3D object detection plays an important role in autonomous driving and other
robotics applications. However, these detectors usually require training on
large amounts of annotated data that is expensive and time-consuming to
collect. Instead, we propose leveraging large amounts of unlabeled point cloud
videos by semi-supervised learning of 3D object detectors via temporal graph
neural networks. Our insight is that temporal smoothing can create more
accurate detection results on unlabeled data, and these smoothed detections can
then be used to retrain the detector. We learn to perform this temporal
reasoning with a graph neural network, where edges represent the relationship
between candidate detections in different time frames. After semi-supervised
learning, our method achieves state-of-the-art detection performance on the
challenging nuScenes and H3D benchmarks, compared to baselines trained on the
same amount of labeled data. Project and code are released at
https://www.jianrenw.com/SOD-TGNN/.
Related papers
- Shelf-Supervised Cross-Modal Pre-Training for 3D Object Detection [52.66283064389691]
State-of-the-art 3D object detectors are often trained on massive labeled datasets.
Recent works demonstrate that self-supervised pre-training with unlabeled data can improve detection accuracy with limited labels.
We propose a shelf-supervised approach for generating zero-shot 3D bounding boxes from paired RGB and LiDAR data.
arXiv Detail & Related papers (2024-06-14T15:21:57Z) - Deep Graph Stream SVDD: Anomaly Detection in Cyber-Physical Systems [17.373668215331737]
We propose a new approach called deep graph vector data description (SVDD) for anomaly detection.
We first use a transformer to preserve both short and long temporal patterns monitoring data in temporal embeddings.
We cluster these embeddings according to sensor type and utilize them to estimate the change in connectivity between various sensors to construct a new weighted graph.
arXiv Detail & Related papers (2023-02-24T22:14:39Z) - Anytime-Lidar: Deadline-aware 3D Object Detection [5.491655566898372]
We propose a scheduling algorithm, which intelligently selects the subset of the components to make effective time and accuracy trade-off on the fly.
We apply our approach to a state-of-art 3D object detection network, PointPillars, and evaluate its performance on Jetson Xavier AGX dataset.
arXiv Detail & Related papers (2022-08-25T16:07:10Z) - 3D Object Detection with a Self-supervised Lidar Scene Flow Backbone [10.341296683155973]
We propose using a self-supervised training strategy to learn a general point cloud backbone model for downstream 3D vision tasks.
Our main contribution leverages learned flow and motion representations and combines a self-supervised backbone with a 3D detection head.
Experiments on KITTI and nuScenes benchmarks show that the proposed self-supervised pre-training increases 3D detection performance significantly.
arXiv Detail & Related papers (2022-05-02T07:53:29Z) - A Lightweight and Detector-free 3D Single Object Tracker on Point Clouds [50.54083964183614]
It is non-trivial to perform accurate target-specific detection since the point cloud of objects in raw LiDAR scans is usually sparse and incomplete.
We propose DMT, a Detector-free Motion prediction based 3D Tracking network that totally removes the usage of complicated 3D detectors.
arXiv Detail & Related papers (2022-03-08T17:49:07Z) - 3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature
Correlation [0.0]
3D-FCT is a Siamese network architecture that utilizes temporal information to simultaneously perform the related tasks of 3D object detection and tracking.
Our proposed method is evaluated on the KITTI tracking dataset where it is shown to provide an improvement of 5.57% mAP over a state-of-the-art approach.
arXiv Detail & Related papers (2021-10-06T06:36:29Z) - AA3DNet: Attention Augmented Real Time 3D Object Detection [0.0]
We propose a novel neural network architecture along with the training and optimization details for detecting 3D objects using point cloud data.
Our method surpasses previous state of the art in this domain both in terms of average precision and speed running at > 30 FPS.
This makes it a feasible option to be deployed in real time applications like self driving cars.
arXiv Detail & Related papers (2021-07-26T12:18:23Z) - ST3D: Self-training for Unsupervised Domain Adaptation on 3D
ObjectDetection [78.71826145162092]
We present a new domain adaptive self-training pipeline, named ST3D, for unsupervised domain adaptation on 3D object detection from point clouds.
Our ST3D achieves state-of-the-art performance on all evaluated datasets and even surpasses fully supervised results on KITTI 3D object detection benchmark.
arXiv Detail & Related papers (2021-03-09T10:51:24Z) - Expandable YOLO: 3D Object Detection from RGB-D Images [64.14512458954344]
This paper aims at constructing a light-weight object detector that inputs a depth and a color image from a stereo camera.
By extending the network architecture of YOLOv3 to 3D in the middle, it is possible to output in the depth direction.
Intersection over Uninon (IoU) in 3D space is introduced to confirm the accuracy of region extraction results.
arXiv Detail & Related papers (2020-06-26T07:32:30Z) - D3Feat: Joint Learning of Dense Detection and Description of 3D Local
Features [51.04841465193678]
We leverage a 3D fully convolutional network for 3D point clouds.
We propose a novel and practical learning mechanism that densely predicts both a detection score and a description feature for each 3D point.
Our method achieves state-of-the-art results in both indoor and outdoor scenarios.
arXiv Detail & Related papers (2020-03-06T12:51:09Z) - Adaptive Anomaly Detection for IoT Data in Hierarchical Edge Computing [71.86955275376604]
We propose an adaptive anomaly detection approach for hierarchical edge computing (HEC) systems to solve this problem.
We design an adaptive scheme to select one of the models based on the contextual information extracted from input data, to perform anomaly detection.
We evaluate our proposed approach using a real IoT dataset, and demonstrate that it reduces detection delay by 84% while maintaining almost the same accuracy as compared to offloading detection tasks to the cloud.
arXiv Detail & Related papers (2020-01-10T05:29:17Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.