Search Results for author:
Found 7 papers, 3 papers with code
OccFeat: Self-supervised Occupancy Feature Prediction for Pretraining BEV Segmentation Networks
Models pretrained with our method exhibit improved BEV semantic segmentation performance, particularly in low-data scenarios.
POP-3D: Open-Vocabulary 3D Occupancy Prediction from Images
We describe an approach to predict open-vocabulary 3D semantic voxel occupancy map from input 2D images with the objective of enabling 3D grounding, segmentation and retrieval of free-form language queries.
3D Semantic Occupancy Prediction
3D Semantic Segmentation
+3
MOCA: Self-supervised Representation Learning by Predicting Masked Online Codebook Assignments
Self-supervised learning can be used for mitigating the greedy needs of Vision Transformer networks for very large fully-annotated datasets.
Unsupervised Object Localization: Observing the Background to Discover Objects
This way, the salient objects emerge as a by-product without any strong assumption on what an object should be.
Drive&Segment: Unsupervised Semantic Segmentation of Urban Scenes via Cross-modal Distillation
This work investigates learning pixel-wise semantic image segmentation in urban scenes without any manual annotation, just from the raw non-curated data collected by cars which, equipped with cameras and LiDAR sensors, drive around a city.
Let's Get Dirty: GAN Based Data Augmentation for Camera Lens Soiling Detection in Autonomous Driving
We propose a novel GAN based algorithm for generating unseen patterns of soiled images.
Detecting Decision Ambiguity from Facial Images
We propose a baseline method combining a deep convolutional neural network with an SVM.
