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Found 6 papers, 4 papers with code
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.
Ranked #4 on
Self-supervised Scene Flow Estimation
on Argoverse 2
Argoverse 2: Next Generation Datasets for Self-Driving Perception and Forecasting
Models are tasked with the prediction of future motion for "scored actors" in each scenario and are provided with track histories that capture object location, heading, velocity, and category.
Neural Prior for Trajectory Estimation
Here, we propose a neural trajectory prior to capture continuous spatio-temporal information without the need for offline data.
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.
Ranked #2 on
Self-supervised Scene Flow Estimation
on Argoverse 2
Scene Flow from Point Clouds with or without Learning
Our proposed objective function can be used with or without learning---as a self-supervisory signal to learn scene flow representations, or as a non-learning-based method in which the scene flow is optimized during runtime.
PointNetLK Revisited
We address the generalization ability of recent learning-based point cloud registration methods.
