Temporal Point Cloud Fusion With Scene Flow for Robust 3D Object Tracking
Author:
Affiliation:
1. School of Vehicle and Mobility, Tsinghua University, Beijing, China
2. National Innovation Center of Intelligent and Connected Vehicles, Beijing, China
Funder
National Key R&D Program of China
National Natural Science Foundation of China
Publisher
Institute of Electrical and Electronics Engineers (IEEE)
Subject
Applied Mathematics,Electrical and Electronic Engineering,Signal Processing
Link
http://xplorestaging.ieee.org/ielx7/97/9686799/09806178.pdf?arnumber=9806178
Reference35 articles.
1. A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation
2. PointNet: Deep hierarchical feature learning on point sets in a metric space;qi;Proc Adv Neural Inf Process Syst,0
3. A learning approach for real-time temporal scene flow estimation from LIDAR data
4. The Pascal Visual Object Classes (VOC) Challenge
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1. Kalman-Based Scene Flow Estimation for Point Cloud Densification and 3D Object Detection in Dynamic Scenes;Sensors;2024-01-31
2. ImFusion: Boosting Two-Stage 3D Object Detection via Image Candidates;IEEE Signal Processing Letters;2024
3. mmMCL3DMOT: Multi-Modal Momentum Contrastive Learning for 3D Multi-Object Tracking;IEEE Signal Processing Letters;2024
4. Saliency Map-Guided End-to-End Image Coding for Machines;IEEE Signal Processing Letters;2024
5. Deep Learning for Scene Flow Estimation on Point Clouds: A Survey and Prospective Trends;Computer Graphics Forum;2023-04-03
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