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Found 5 papers, 4 papers with code
GACE: Geometry Aware Confidence Enhancement for Black-Box 3D Object Detectors on LiDAR-Data
Widely-used LiDAR-based 3D object detectors often neglect fundamental geometric information readily available from the object proposals in their confidence estimation.
MAELi: Masked Autoencoder for Large-Scale LiDAR Point Clouds
The sensing process of large-scale LiDAR point clouds inevitably causes large blind spots, i. e. regions not visible to the sensor.
OccAM's Laser: Occlusion-based Attribution Maps for 3D Object Detectors on LiDAR Data
These maps indicate the importance of each 3D point in predicting the specific objects.
MURAUER: Mapping Unlabeled Real Data for Label AUstERity
In this work, we remove this requirement by learning to map from the features of real data to the features of synthetic data mainly using a large amount of synthetic and unlabeled real data.
Learning Pose Specific Representations by Predicting Different Views
To exploit this observation, we train a model that -- given input from one view -- estimates a latent representation, which is trained to be predictive for the appearance of the object when captured from another viewpoint.
