Search Results for author:
Found 9 papers, 8 papers with code
DistillBEV: Boosting Multi-Camera 3D Object Detection with Cross-Modal Knowledge Distillation
In this work, we propose to boost the representation learning of a multi-camera BEV based student detector by training it to imitate the features of a well-trained LiDAR based teacher detector.
PillarNeXt: Rethinking Network Designs for 3D Object Detection in LiDAR Point Clouds
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-grained geometrical modeling.
Ranked #1 on
3D Object Detection
on waymo vehicle
Exploring Simple 3D Multi-Object Tracking for Autonomous Driving
3D multi-object tracking in LiDAR point clouds is a key ingredient for self-driving vehicles.
Self-Supervised Pillar Motion Learning for Autonomous Driving
Autonomous driving can benefit from motion behavior comprehension when interacting with diverse traffic participants in highly dynamic environments.
Probabilistic Multi-modal Trajectory Prediction with Lane Attention for Autonomous Vehicles
As for vehicles, their trajectories are significantly influenced by the lane geometry and how to effectively use the lane information is of active interest.
Grouped Spatial-Temporal Aggregation for Efficient Action Recognition
This decomposition is more parameter-efficient and enables us to quantitatively analyze the contributions of spatial and temporal features in different layers.
OriNet: A Fully Convolutional Network for 3D Human Pose Estimation
We use limb orientations as a new way to represent 3D poses and bind the orientation together with the bounding box of each limb region to better associate images and predictions.
Ranked #76 on
3D Human Pose Estimation
on MPI-INF-3DHP
(AUC metric)
Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding
Performance on the five tasks of depth estimation, optical flow estimation, odometry, moving object segmentation and scene flow estimation shows that our approach outperforms other SoTA methods.
Joint Unsupervised Learning of Optical Flow and Depth by Watching Stereo Videos
Then the whole scene is decomposed into moving foreground and static background by compar- ing the estimated optical flow and rigid flow derived from the depth and ego-motion.
