29 citations · 57 across the 5 of their papers we have counts for
5 papers
Labels Are Not Perfect: Inferring Spatial Uncertainty in Object Detection
Di Feng, Zining Wang, Yiyang Zhou +5
The availability of many real-world driving datasets is a key reason behind the recent progress of object detection algorithms in autonomous driving. However, there exist ambiguity…
Towards Better Performance and More Explainable Uncertainty for 3D Object Detection of Autonomous Vehicles
Hujie Pan, Zining Wang, Wei Zhan +1
In this paper, we propose a novel form of the loss function to increase the performance of LiDAR-based 3d object detection and obtain more explainable and convincing uncertainty fo…
SqueezeSegV3: Spatially-Adaptive Convolution for Efficient Point-Cloud Segmentation
Chenfeng Xu, Bichen Wu, Zining Wang +4
LiDAR point-cloud segmentation is an important problem for many applications. For large-scale point cloud segmentation, the \textit{de facto} method is to project a 3D point cloud…
Inferring Spatial Uncertainty in Object Detection
Zining Wang, Di Feng, Yiyang Zhou +5
The availability of real-world datasets is the prerequisite for developing object detection methods for autonomous driving. While ambiguity exists in object labels due to error-pro…
Fusing Bird View LIDAR Point Cloud and Front View Camera Image for Deep Object Detection
Zining Wang, Wei Zhan, Masayoshi Tomizuka
We propose a new method for fusing a LIDAR point cloud and camera-captured images in the deep convolutional neural network (CNN). The proposed method constructs a new layer called…