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Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions
Carlos A. Diaz-Ruiz;Youya Xia;Yurong You;Jose Nino;Junan Chen;Josephine Monica;Xiangyu Chen;Katie Luo;Yan Wang;Marc Emond;Wei-Lun Chao;Bharath Hariharan;Kilian Q. Weinberger;Mark Campbell
2022 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2022 | Conference Paper |
Cited by: Papers (25)
Advances in perception for self-driving cars have accelerated in recent years due to the availability of large-scale datasets, typically collected at specific locations and under nice weather conditions. Yet, to achieve the high safety requirement, these perceptual systems must operate robustly under a wide variety of weather conditions including snow and rain. In this paper, we present a new data...Show More
Ithaca365: Dataset and Driving Perception under Repeated and Challenging Weather Conditions
Carlos A. Diaz-Ruiz;Youya Xia;Yurong You;Jose Nino;Junan Chen;Josephine Monica;Xiangyu Chen;Katie Luo;Yan Wang;Marc Emond;Wei-Lun Chao;Bharath Hariharan;Kilian Q. Weinberger;Mark Campbell
Year: 2022 | Conference Paper |
Exploiting Playbacks in Unsupervised Domain Adaptation for 3D Object Detection in Self-Driving Cars
Yurong You;Carlos Andres Diaz-Ruiz;Yan Wang;Wei-Lun Chao;Bharath Hariharan;Mark Campbell;Kilian Q Weinberger
2022 International Conference on Robotics and Automation (ICRA)
Year: 2022 | Conference Paper |
Cited by: Papers (15)
Self-driving cars must detect other traffic participants like vehicles and pedestrians in 3D in order to plan safe routes and avoid collisions. State-of-the-art 3D object detectors, based on deep learning, have shown promising accuracy but are prone to over-fit domain idiosyncrasies, making them fail in new environments-a serious problem for the robustness of self-driving cars. In this paper, we p...Show More
Deep Co-Training with Task Decomposition for Semi-Supervised Domain Adaptation
Luyu Yang;Yan Wang;Mingfei Gao;Abhinav Shrivastava;Kilian Q. Weinberger;Wei-Lun Chao;Ser-Nam Lim
2021 IEEE/CVF International Conference on Computer Vision (ICCV)
Year: 2021 | Conference Paper |
Cited by: Papers (47)
Semi-supervised domain adaptation (SSDA) aims to adapt models trained from a labeled source domain to a different but related target domain, from which unlabeled data and a small set of labeled data are provided. Current methods that treat source and target supervision without distinction overlook their inherent discrepancy, resulting in a source-dominated model that has not effectively use the ta...Show More
PLUMENet: Efficient 3D Object Detection from Stereo Images
2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS)
Year: 2021 | Conference Paper |
Cited by: Papers (22)
3D object detection is a key component of many robotic applications such as self-driving vehicles. While many approaches rely on expensive 3D sensors such as LiDAR to produce accurate 3D estimates, methods that exploit stereo cameras have recently shown promising results at a lower cost. Existing approaches tackle this problem in two steps: first depth estimation from stereo images is performed to...Show More
Spatial-Temporal Graph Neural Network For Interaction-Aware Vehicle Trajectory Prediction
2021 IEEE 17th International Conference on Automation Science and Engineering (CASE)
Year: 2021 | Conference Paper |
Cited by: Papers (6)
In this paper, a Spatial Temporal Graph Neural Network (STGNN) model is developed, including a temporal block and Graph Neural Network (GNN) block, to solve the problem of vehicle trajectory prediction in unstructured scenes. Specifically, a temporal block combines a recurrent neural network and non-local operation to extract the features from past trajectories, and a GNN block models the subtle i...Show More
End-to-End Pseudo-LiDAR for Image-Based 3D Object Detection
Rui Qian;Divyansh Garg;Yan Wang;Yurong You;Serge Belongie;Bharath Hariharan;Mark Campbell;Kilian Q. Weinberger;Wei-Lun Chao
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (130)
Reliable and accurate 3D object detection is a necessity for safe autonomous driving. Although LiDAR sensors can provide accurate 3D point cloud estimates of the environment, they are also prohibitively expensive for many settings. Recently, the introduction of pseudo-LiDAR (PL) has led to a drastic reduction in the accuracy gap between methods based on LiDAR sensors and those based on cheap stere...Show More
Train in Germany, Test in the USA: Making 3D Object Detectors Generalize
Yan Wang;Xiangyu Chen;Yurong You;Li Erran Li;Bharath Hariharan;Mark Campbell;Kilian Q. Weinberger;Wei-Lun Chao
2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2020 | Conference Paper |
Cited by: Papers (96)
In the domain of autonomous driving, deep learning has substantially improved the 3D object detection accuracy for LiDAR and stereo camera data alike. While deep networks are great at generalization, they are also notorious to overfit to all kinds of spurious artifacts, such as brightness, car sizes and models, that may appear consistently throughout the data. In fact, most datasets for autonomous...Show More
Pseudo-LiDAR From Visual Depth Estimation: Bridging the Gap in 3D Object Detection for Autonomous Driving
Yan Wang;Wei-Lun Chao;Divyansh Garg;Bharath Hariharan;Mark Campbell;Kilian Q. Weinberger
2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR)
Year: 2019 | Conference Paper |
Cited by: Papers (702)
3D object detection is an essential task in autonomous driving. Recent techniques excel with highly accurate detection rates, provided the 3D input data is obtained from precise but expensive LiDAR technology. Approaches based on cheaper monocular or stereo imagery data have, until now, resulted in drastically lower accuracies --- a gap that is commonly attributed to poor image-based depth estimat...Show More
Vision-only 3D Tracking for Self-Driving Cars
Carlos Diaz-Ruiz;Yan Wang;Wei-Lun Chao;Kilian Weinberger;Mark Campbell
2019 IEEE 15th International Conference on Automation Science and Engineering (CASE)
Year: 2019 | Conference Paper |
Cited by: Papers (4)
A vision-only tracking framework is developed and experimentally evaluated for self-driving cars. Vision-only object detection is achieved by transforming stereo depth maps into point clouds, followed by LiDAR-based detectors. Each detection yields location, orientation, and object size. A tracking algorithm is then used to combine the detections with a physics based model to create robust vehicle...Show More
Anytime Stereo Image Depth Estimation on Mobile Devices
Yan Wang;Zihang Lai;Gao Huang;Brian H. Wang;Laurens van der Maaten;Mark Campbell;Kilian Q. Weinberger
2019 International Conference on Robotics and Automation (ICRA)
Year: 2019 | Conference Paper |
Many applications of stereo depth estimation in robotics require the generation of accurate disparity maps in real time under significant computational constraints. Current state-of-the-art algorithms force a choice between either generating accurate mappings at a slow pace, or quickly generating inaccurate ones, and additionally these methods typically require far too many parameters to be usable...Show More
LDLS: 3-D Object Segmentation Through Label Diffusion From 2-D Images
Brian H. Wang;Wei-Lun Chao;Yan Wang;Bharath Hariharan;Kilian Q. Weinberger;Mark Campbell
Object segmentation in three-dimensional (3-D) point clouds is a critical task for robots capable of 3-D perception. Despite the impressive performance of deep learning-based approaches on object segmentation in 2-D images, deep learning has not been applied nearly as successfully for 3-D point cloud segmentation. Deep networks generally require large amounts of labeled training data, which are re...Show More
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