Scene Flow Estimation

47 papers with code • 4 benchmarks • 4 datasets

Scene Flow Estimation is the task of obtaining 3D structure and 3D motion of dynamic scenes, which is crucial to environment perception, e.g., in the context of autonomous navigation.

Source: Self-Supervised Monocular Scene Flow Estimation

Most implemented papers

A Large Dataset to Train Convolutional Networks for Disparity, Optical Flow, and Scene Flow Estimation


By combining a flow and disparity estimation network and training it jointly, we demonstrate the first scene flow estimation with a convolutional network.

HPLFlowNet: Hierarchical Permutohedral Lattice FlowNet for Scene Flow Estimation on Large-scale Point Clouds

laoreja/HPLFlowNet CVPR 2019

We present a novel deep neural network architecture for end-to-end scene flow estimation that directly operates on large-scale 3D point clouds.

MeteorNet: Deep Learning on Dynamic 3D Point Cloud Sequences

xingyul/meteornet ICCV 2019

Understanding dynamic 3D environment is crucial for robotic agents and many other applications.

Scalable Scene Flow from Point Clouds in the Real World

leolyj/dca-srsfe 1 Mar 2021

In this work, we introduce a new large-scale dataset for scene flow estimation derived from corresponding tracked 3D objects, which is $\sim$1, 000$\times$ larger than previous real-world datasets in terms of the number of annotated frames.

Accurate Point Cloud Registration with Robust Optimal Transport

uncbiag/shapmagn NeurIPS 2021

Finally, we showcase the performance of transport-enhanced registration models on a wide range of challenging tasks: rigid registration for partial shapes; scene flow estimation on the Kitti dataset; and nonparametric registration of lung vascular trees between inspiration and expiration.

Self-Supervised Scene Flow Estimation with 4-D Automotive Radar

toytiny/raflow 2 Mar 2022

Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy.

Learning Rigidity in Dynamic Scenes with a Moving Camera for 3D Motion Field Estimation

NVlabs/learningrigidity ECCV 2018

Estimation of 3D motion in a dynamic scene from a temporal pair of images is a core task in many scene understanding problems.

SceneEDNet: A Deep Learning Approach for Scene Flow Estimation

ravikt/sceneednet 10 Jul 2018

This paper introduces a first effort to apply a deep learning method for direct estimation of scene flow by presenting a fully convolutional neural network with an encoder-decoder (ED) architecture.

Occlusions, Motion and Depth Boundaries with a Generic Network for Disparity, Optical Flow or Scene Flow Estimation

FilippoAleotti/Dwarf-Tensorflow ECCV 2018

Making use of the estimated occlusions, we also show improved results on motion segmentation and scene flow estimation.

Every Pixel Counts ++: Joint Learning of Geometry and Motion with 3D Holistic Understanding

chenxuluo/EPC 14 Oct 2018

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.