74 citations · 133 across the 12 of their papers we have counts for
7 papers · 1 filter
Image-Guided Semantic Pseudo-LiDAR Point Generation for 3D Object Detection
Minseung Lee, Seokha Moon, Seung Joon Lee +2
In autonomous driving scenarios, accurate perception is becoming an even more critical task for safe navigation. While LiDAR provides precise spatial data, its inherent sparsity ma…
VisionTrap: Vision-Augmented Trajectory Prediction Guided by Textual Descriptions
Seokha Moon, Hyun Woo, Hongbeen Park +6
Predicting future trajectories for other road agents is an essential task for autonomous vehicles. Established trajectory prediction methods primarily use agent tracks generated by…
Instance Segmentation with Cross-Modal Consistency
Alex Zihao Zhu, Vincent Casser, Reza Mahjourian +2
Segmenting object instances is a key task in machine perception, with safety-critical applications in robotics and autonomous driving. We introduce a novel approach to instance seg…
Unsupervised Monocular Depth and Ego-motion Learning with Structure and Semantics
Vincent Casser, Soeren Pirk, Reza Mahjourian +1
We present an approach which takes advantage of both structure and semantics for unsupervised monocular learning of depth and ego-motion. More specifically, we model the motion of…
Future Segmentation Using 3D Structure
Suhani Vora, Reza Mahjourian, Soeren Pirk +1
Predicting the future to anticipate the outcome of events and actions is a critical attribute of autonomous agents; particularly for agents which must rely heavily on real time vis…
Depth Prediction Without the Sensors: Leveraging Structure for Unsupervised Learning from Monocular Videos
Vincent Casser, Soeren Pirk, Reza Mahjourian +1
Learning to predict scene depth from RGB inputs is a challenging task both for indoor and outdoor robot navigation. In this work we address unsupervised learning of scene depth and…
Unsupervised Learning of Depth and Ego-Motion from Monocular Video Using 3D Geometric Constraints
Reza Mahjourian, Martin Wicke, Anelia Angelova
We present a novel approach for unsupervised learning of depth and ego-motion from monocular video. Unsupervised learning removes the need for separate supervisory signals (depth o…