Search Results for author:
Found 9 papers, 4 papers with code
Language-Guided Instance-Aware Domain-Adaptive Panoptic Segmentation
A key challenge in panoptic UDA is reducing the domain gap between a labeled source and an unlabeled target domain while harmonizing the subtasks of semantic and instance segmentation to limit catastrophic interference.
2D Feature Distillation for Weakly- and Semi-Supervised 3D Semantic Segmentation
As 3D perception problems grow in popularity and the need for large-scale labeled datasets for LiDAR semantic segmentation increase, new methods arise that aim to reduce the necessity for dense annotations by employing weakly-supervised training.
Discwise Active Learning for LiDAR Semantic Segmentation
Finally we propose a semi-supervised learning approach to utilize all frames within our dataset and improve performance.
Three Ways to Improve Verbo-visual Fusion for Dense 3D Visual Grounding
A common formulation to tackle 3D visual grounding is grounding-by-detection, where localization is done via bounding boxes.
LiDAR Meta Depth Completion
While using a single model, our method yields significantly better results than a non-adaptive baseline trained on different LiDAR patterns.
Scribble-Supervised LiDAR Semantic Segmentation
Densely annotating LiDAR point clouds remains too expensive and time-consuming to keep up with the ever growing volume of data.
Ranked #1 on
3D Semantic Segmentation
on ScribbleKITTI
End-To-End Optimization of LiDAR Beam Configuration for 3D Object Detection and Localization
In this work, we take a new route to learn to optimize the LiDAR beam configuration for a given application.
Understanding Bird's-Eye View of Road Semantics using an Onboard Camera
In this work, we study scene understanding in the form of online estimation of semantic BEV maps using the video input from a single onboard camera.
Improving Point Cloud Semantic Segmentation by Learning 3D Object Detection
Point cloud semantic segmentation plays an essential role in autonomous driving, providing vital information about drivable surfaces and nearby objects that can aid higher level tasks such as path planning and collision avoidance.
