243 citations · 416 across the 17 of their papers we have counts for
10 papers · 2 filters
Learning Joint 2D-3D Representations for Depth Completion
Yun Chen, Bin Yang, Ming Liang +1
In this paper, we tackle the problem of depth completion from RGBD data. Towards this goal, we design a simple yet effective neural network block that learns to extract joint 2D an…
Multi-Task Multi-Sensor Fusion for 3D Object Detection
Ming Liang, Bin Yang, Yun Chen +2
In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object detection. Towards this goal we present an end-to-end learnable architecture that rea…
HDNET: Exploiting HD Maps for 3D Object Detection
Bin Yang, Ming Liang, Raquel Urtasun
In this paper we show that High-Definition (HD) maps provide strong priors that can boost the performance and robustness of modern 3D object detectors. Towards this goal, we design…
Recovering and Simulating Pedestrians in the Wild
Ze Yang, Siva Manivasagam, Ming Liang +3
Sensor simulation is a key component for testing the performance of self-driving vehicles and for data augmentation to better train perception systems. Typical approaches rely on a…
V2VNet: Vehicle-to-Vehicle Communication for Joint Perception and Prediction
Tsun-Hsuan Wang, Sivabalan Manivasagam, Ming Liang +4
In this paper, we explore the use of vehicle-to-vehicle (V2V) communication to improve the perception and motion forecasting performance of self-driving vehicles. By intelligently…
Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
Kelvin Wong, Qiang Zhang, Ming Liang +4
We present a novel method for testing the safety of self-driving vehicles in simulation. We propose an alternative to sensor simulation, as sensor simulation is expensive and has l…
End-to-end Contextual Perception and Prediction with Interaction Transformer
Lingyun Luke Li, Bin Yang, Ming Liang +4
In this paper, we tackle the problem of detecting objects in 3D and forecasting their future motion in the context of self-driving. Towards this goal, we design a novel approach th…
Learning Lane Graph Representations for Motion Forecasting
Ming Liang, Bin Yang, Rui Hu +4
We propose a motion forecasting model that exploits a novel structured map representation as well as actor-map interactions. Instead of encoding vectorized maps as raster images, w…
PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
Ming Liang, Bin Yang, Wenyuan Zeng +4
We tackle the problem of joint perception and motion forecasting in the context of self-driving vehicles. Towards this goal we propose PnPNet, an end-to-end model that takes as inp…
Physically Realizable Adversarial Examples for LiDAR Object Detection
James Tu, Mengye Ren, Siva Manivasagam +5
Modern autonomous driving systems rely heavily on deep learning models to process point cloud sensory data; meanwhile, deep models have been shown to be susceptible to adversarial…