Search Results for author:
Found 9 papers, 5 papers with code
Let It Flow: Simultaneous Optimization of 3D Flow and Object Clustering
We identified the structural constraints and the use of large and strict rigid clusters as the main pitfall of the current approaches and we propose a novel clustering approach that allows for combination of overlapping soft clusters as well as non-overlapping rigid clusters representation.
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
Regularizing Self-supervised 3D Scene Flows with Surface Awareness and Cyclic Consistency
Learning without supervision how to predict 3D scene flows from point clouds is essential to many perception systems.
T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds
Deep perception models have to reliably cope with an open-world setting of domain shifts induced by different geographic regions, sensor properties, mounting positions, and several other reasons.
Teachers in concordance for pseudo-labeling of 3D sequential data
We propose to leverage sequences of point clouds to boost the pseudolabeling technique in a teacher-student setup via training multiple teachers, each with access to different temporal information.
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.
Artificial Dummies for Urban Dataset Augmentation
This is achieved with a data generator (called DummyNet) with disentangled control of the pose, the appearance, and the target background scene.
Yes, we GAN: Applying Adversarial Techniques for Autonomous Driving
Generative Adversarial Networks (GAN) have gained a lot of popularity from their introduction in 2014 till present.
Challenges in Designing Datasets and Validation for Autonomous Driving
There is a large gap between academic and industrial setting and a substantial way from a research prototype, built on public datasets, to a deployable solution which is a challenging task.
