63 citations · 152 across the 10 of their papers we have counts for
10 papers
Cross-Modal Self-Supervised Learning with Effective Contrastive Units for LiDAR Point Clouds
Mu Cai, Chenxu Luo, Yong Jae Lee +1
3D perception in LiDAR point clouds is crucial for a self-driving vehicle to properly act in 3D environment. However, manually labeling point clouds is hard and costly. There has b…
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation
Zeyu Wang, Dingwen Li, Chenxu Luo +2
3D perception based on the representations learned from multi-camera bird's-eye-view (BEV) is trending as cameras are cost-effective for mass production in autonomous driving indus…
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds
Jinyu Li, Chenxu Luo, Xiaodong Yang
In order to deal with the sparse and unstructured raw point clouds, LiDAR based 3D object detection research mostly focuses on designing dedicated local point aggregators for fine-…
Exploring Simple 3D Multi-Object Tracking for Autonomous Driving
Chenxu Luo, Xiaodong Yang, Alan Yuille
3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles. Existing methods are predominantly based on the tracking-by-detection pipeline and ine…
Self-Supervised Pillar Motion Learning for Autonomous Driving
Chenxu Luo, Xiaodong Yang, Alan Yuille
Autonomous driving can benefit from motion behavior comprehension when interacting with diverse traffic participants in highly dynamic environments. Recently, there has been a grow…
Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles
Chenxu Luo, Lin Sun, Dariush Dabiri +1
Trajectory prediction is crucial for autonomous vehicles. The planning system not only needs to know the current state of the surrounding objects but also their possible states in…
Grouped Spatial-Temporal Aggregation for Efficient Action Recognition
Chenxu Luo, Alan Yuille
Temporal reasoning is an important aspect of video analysis. 3D CNN shows good performance by exploring spatial-temporal features jointly in an unconstrained way, but it also incre…
OriNet: A Fully Convolutional Network for 3D Human Pose Estimation
Chenxu Luo, Xiao Chu, Alan Yuille
In this paper, we propose a fully convolutional network for 3D human pose estimation from monocular images. We use limb orientations as a new way to represent 3D poses and bind the…
Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos
Yang Wang, Zhenheng Yang, Peng Wang +3
Learning depth and optical flow via deep neural networks by watching videos has made significant progress recently. In this paper, we jointly solve the two tasks by exploiting the…
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
Chenxu Luo, Zhenheng Yang, Peng Wang +4
Learning to estimate 3D geometry in a single frame and optical flow from consecutive frames by watching unlabeled videos via deep convolutional network has made significant progres…