Self-supervised Scene Flow Estimation
11 papers with code • 1 benchmarks • 1 datasets
Self-supervised method only with lidar point clouds as input to predict flows of each point.
Self-supervised way for scene flow estimation
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Most implemented papers
Self-Supervised Scene Flow Estimation with 4-D Automotive Radar
Scene flow allows autonomous vehicles to reason about the arbitrary motion of multiple independent objects which is the key to long-term mobile autonomy.
PointPWC-Net: A Coarse-to-Fine Network for Supervised and Self-Supervised Scene Flow Estimation on 3D Point Clouds
We propose a novel end-to-end deep scene flow model, called PointPWC-Net, on 3D point clouds in a coarse-to-fine fashion.
Just Go with the Flow: Self-Supervised Scene Flow Estimation
When interacting with highly dynamic environments, scene flow allows autonomous systems to reason about the non-rigid motion of multiple independent objects.
Adversarial Self-Supervised Scene Flow Estimation
This work proposes a metric learning approach for self-supervised scene flow estimation.
FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation
Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision.
Occlusion Guided Self-supervised Scene Flow Estimation on 3D Point Clouds
Understanding the flow in 3D space of sparsely sampled points between two consecutive time frames is the core stone of modern geometric-driven systems such as VR/AR, Robotics, and Autonomous driving.
Neural Scene Flow Prior
A central innovation here is the inclusion of a neural scene flow prior, which uses the architecture of neural networks as a new type of implicit regularizer.
3D Object Detection with a Self-supervised Lidar Scene Flow Backbone
Our main contribution leverages learned flow and motion representations and combines a self-supervised backbone with a supervised 3D detection head.
Fast Neural Scene Flow
Neural Scene Flow Prior (NSFP) is of significant interest to the vision community due to its inherent robustness to out-of-distribution (OOD) effects and its ability to deal with dense lidar points.
ZeroFlow: Scalable Scene Flow via Distillation
Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds.