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Found 8 papers, 4 papers with code
ICP-Flow: LiDAR Scene Flow Estimation with ICP
We incorporate this rigid-motion assumption into our design, where the goal is to associate objects over scans and then estimate the locally rigid transformations.
BaSAL: Size-Balanced Warm Start Active Learning for LiDAR Semantic Segmentation
By sampling object clusters according to their size, we can thus create a size-balanced dataset that is also more class-balanced.
A step towards understanding why classification helps regression
A number of computer vision deep regression approaches report improved results when adding a classification loss to the regression loss.
Deep vanishing point detection: Geometric priors make dataset variations vanish
Deep learning has improved vanishing point detection in images.
NeRD++: Improved 3D-mirror symmetry learning from a single image
Experiments on both synthetic and real-world datasets show the benefit of our proposed changes for improved data efficiency and inference speed.
Investigating transformers in the decomposition of polygonal shapes as point collections
Transformers can generate predictions in two approaches: 1. auto-regressively by conditioning each sequence element on the previous ones, or 2. directly produce an output sequences in parallel.
Semi-supervised lane detection with Deep Hough Transform
Current work on lane detection relies on large manually annotated datasets.
Deep Hough-Transform Line Priors
Here, we reduce the dependency on labeled data by building on the classic knowledge-based priors while using deep networks to learn features.
Ranked #3 on
Line Segment Detection
on wireframe dataset
