3D Geometry Perception
4 papers with code • 0 benchmarks • 3 datasets
Image: Zhao et al
These leaderboards are used to track progress in 3D Geometry Perception
SIGNet is shown to improve upon the state-of-the-art unsupervised learning for depth prediction by 30% (in squared relative error).
Recently, the rapid development of Solid-State LiDAR (SSL) enables low-cost and efficient obtainment of 3D point clouds from the environment, which has inspired a large quantity of studies and applications.
ConDor is a self-supervised method that learns to Canonicalize the 3D orientation and position for full and partial 3D point clouds.