Search Results for author:
Found 5 papers, 2 papers with code
LMD: Light-weight Prediction Quality Estimation for Object Detection in Lidar Point Clouds
In order to address these two issues, we propose LidarMetaDetect (LMD), a light-weight post-processing scheme for prediction quality estimation.
Fake it, Mix it, Segment it: Bridging the Domain Gap Between Lidar Sensors
We compare our method with two state of the art approaches for semantic lidar segmentation domain adaptation with a significant improvement for unsupervised and semi-supervised domain adaptation.
False Positive Detection and Prediction Quality Estimation for LiDAR Point Cloud Segmentation
We present a novel post-processing tool for semantic segmentation of LiDAR point cloud data, called LidarMetaSeg, which estimates the prediction quality segmentwise.
MetaBox+: A new Region Based Active Learning Method for Semantic Segmentation using Priority Maps
The comparison and analysis of the results provide insights into annotation costs as well as robustness and variance of the methods.
Prediction Error Meta Classification in Semantic Segmentation: Detection via Aggregated Dispersion Measures of Softmax Probabilities
We aggregate these dispersion measures segment-wise and derive metrics that are well-correlated with the segment-wise IoU of prediction and ground truth.
