103 citations · 328 across the 79 of their papers we have counts for
13 papers · 1 filter
Relationally Grounded Latent World Models for Autonomous Driving
Fabian Schmidt, Markus Enzweiler, Abhinav Valada
Latent world models learn predictive representations for autonomous driving, but the relational semantics these states preserve often remain implicit. We investigate whether traffi…
GhostPoint: Self-Supervised Representation Learning by Hallucinating Occluded LiDAR Structure
Mohamed Abdelsamad, Bin Yang, Michael Ulrich +4
3D object detection from LiDAR point clouds is a core problem in autonomous driving. Recent advances in self-supervised learning (SSL) enable scalable pretraining and transfers wel…
BLINK: Behavioral Latent Modeling of NK Cell Cytotoxicity
Iman Nematollahi, Jose Francisco Villena-Ossa, Alina Moter +6
Machine learning models of cellular interaction dynamics hold promise for understanding cell behavior. Natural killer (NK) cell cytotoxicity is a prominent example of such interact…
ConfCtrl: Enabling Precise Camera Control in Video Diffusion via Confidence-Aware Interpolation
Liudi Yang, George Eskandar, Fengyi Shen +5
We address the challenge of novel view synthesis from only two input images under large viewpoint changes. Existing regression-based methods lack the capacity to reconstruct unseen…
Class-Incremental Motion Forecasting
Nicolas Schischka, Nikhil Gosala, B Ravi Kiran +2
Motion forecasting enables autonomous vehicles to anticipate scene evolution by predicting the future trajectories of dynamic agents. However, existing approaches typically assume…
UP-Fuse: Uncertainty-guided LiDAR-Camera Fusion for 3D Panoptic Segmentation
Rohit Mohan, Florian Drews, Yakov Miron +2
LiDAR-camera fusion enhances 3D panoptic segmentation by leveraging camera images to complement sparse LiDAR scans, but it also introduces a critical failure mode. Under adverse co…
CoVAR: Co-generation of Video and Action for Robotic Manipulation via Multi-Modal Diffusion
Liudi Yang, Yang Bai, George Eskandar +5
We present a method to generate video-action pairs that follow text instructions, starting from an initial image observation and the robot's joint states. Our approach automaticall…
Enhancing LLM-based Autonomous Driving with Modular Traffic Light and Sign Recognition
Fabian Schmidt, Noushiq Mohammed Kayilan Abdul Nazar, Markus Enzweiler +1
Large Language Models (LLMs) are increasingly used for decision-making and planning in autonomous driving, showing promising reasoning capabilities and potential to generalize acro…
GraphPilot: Grounded Scene Graph Conditioning for Language-Based Autonomous Driving
Fabian Schmidt, Markus Enzweiler, Abhinav Valada
Vision-language models have recently emerged as promising planners for autonomous driving, where success hinges on reasoning over spatial structure and dynamic interactions from mu…
RoboEnvision: A Long-Horizon Video Generation Model for Multi-Task Robot Manipulation
Liudi Yang, Yang Bai, George Eskandar +7
We address the problem of generating long-horizon videos for robotic manipulation tasks. Text-to-video diffusion models have made significant progress in photorealism, language und…
OpenLex3D: A Tiered Evaluation Benchmark for Open-Vocabulary 3D Scene Representations
Christina Kassab, Sacha Morin, Martin Büchner +5
3D scene understanding has been transformed by open-vocabulary language models that enable interaction via natural language. However, at present the evaluation of these representat…
The Bare Necessities: Designing Simple, Effective Open-Vocabulary Scene Graphs
Christina Kassab, Matías Mattamala, Sacha Morin +4
3D open-vocabulary scene graph methods are a promising map representation for embodied agents, however many current approaches are computationally expensive. In this paper, we reex…
7th AI Driving Olympics: 1st Place Report for Panoptic Tracking
Rohit Mohan, Abhinav Valada
In this technical report, we describe our EfficientLPT architecture that won the panoptic tracking challenge in the 7th AI Driving Olympics at NeurIPS 2021. Our architecture builds…