Raquel Urtasun

収録論文 72本 ・ フィジカルAI/ロボット学習

※arXiv著者名で収集。同姓同名の別人の論文が含まれる場合があります。

論文

• Traffic Scenario Orchestration from Language via Constraint Satisfaction
  2026/5/1
• Conditional Flow-VAE for Safety-Critical Traffic Scenario Generation
  2026/5/1
• Diffusion-guided Generalizable Enhancer for Urban Scene Reconstruction
  2026/5/1
• GenAssets: Generating in-the-wild 3D Assets in Latent Space
  2026/4/1
• Efficient Equivariant Transformer for Self-Driving Agent Modeling
  2026/4/1
• FOMO-3D: Using Vision Foundation Models for Long-Tailed 3D Object Detection
  2026/3/1
• Flux4D: Flow-based Unsupervised 4D Reconstruction
  2025/12/1
• SaLF: Sparse Local Fields for Multi-Sensor Rendering in Real-Time
  2025/7/1
• Learning to Drive via Asymmetric Self-Play
  2024/9/1
• G3R: Gradient Guided Generalizable Reconstruction
  2024/9/1
• UniCal: Unified Neural Sensor Calibration
  2024/9/1
• UnO: Unsupervised Occupancy Fields for Perception and Forecasting
  2024/6/1
• DeTra: A Unified Model for Object Detection and Trajectory Forecasting
  2024/6/1
• QuAD: Query-based Interpretable Neural Motion Planning for Autonomous Driving
  2024/4/1
• Adv3D: Generating Safety-Critical 3D Objects through Closed-Loop Simulation
  2023/11/1
• UltraLiDAR: Learning Compact Representations for LiDAR Completion and Generation
  2023/11/1
• Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion
  2023/11/1
• 4D-Former: Multimodal 4D Panoptic Segmentation
  2023/11/1
• Reconstructing Objects in-the-wild for Realistic Sensor Simulation
  2023/11/1
• MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory
  2023/11/1
• Towards Unsupervised Object Detection From LiDAR Point Clouds
  2023/11/1
• Learning Realistic Traffic Agents in Closed-loop
  2023/11/1
• LabelFormer: Object Trajectory Refinement for Offboard Perception from LiDAR Point Clouds
  2023/11/1
• CADSim: Robust and Scalable in-the-wild 3D Reconstruction for Controllable Sensor Simulation
  2023/11/1
• Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving
  2023/8/1
• UniSim: A Neural Closed-Loop Sensor Simulator
  2023/8/1
• Rethinking Closed-loop Training for Autonomous Driving
  2023/6/1
• GoRela: Go Relative for Viewpoint-Invariant Motion Forecasting
  2022/11/1
• Virtual Correspondence: Humans as a Cue for Extreme-View Geometry
  2022/6/1
• Just Label What You Need: Fine-Grained Active Selection for Perception and Prediction through Partially Labeled Scenes
  2021/4/1
• Deep Parametric Continuous Convolutional Neural Networks
  2021/1/1
• MP3: A Unified Model to Map, Perceive, Predict and Plan
  2021/1/1
• LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving
  2021/1/1
• Asynchronous Multi-View SLAM
  2021/1/1
• GeoSim: Realistic Video Simulation via Geometry-Aware Composition for Self-Driving
  2021/1/1
• Deep Structured Reactive Planning
  2021/1/1
• Deep Multi-Task Learning for Joint Localization, Perception, and Prediction
  2021/1/1
• AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles
  2021/1/1
• Safety-Oriented Pedestrian Motion and Scene Occupancy Forecasting
  2021/1/1
• IntentNet: Learning to Predict Intention from Raw Sensor Data
  2021/1/1
• Deep Feedback Inverse Problem Solver
  2021/1/1
• SceneGen: Learning to Generate Realistic Traffic Scenes
  2021/1/1
• Diverse Complexity Measures for Dataset Curation in Self-driving
  2021/1/1
• TrafficSim: Learning to Simulate Realistic Multi-Agent Behaviors
  2021/1/1
• LaneRCNN: Distributed Representations for Graph-Centric Motion Forecasting
  2021/1/1
• End-to-end Interpretable Neural Motion Planner
  2021/1/1
• Learning to Localize Through Compressed Binary Maps
  2020/12/1
• Pit30M: A Benchmark for Global Localization in the Age of Self-Driving Cars
  2020/12/1
• DAGMapper: Learning to Map by Discovering Lane Topology
  2020/12/1
• Learning to Localize Using a LiDAR Intensity Map
  2020/12/1
• Universal Embeddings for Spatio-Temporal Tagging of Self-Driving Logs
  2020/11/1
• Learning to Communicate and Correct Pose Errors
  2020/11/1
• Recovering and Simulating Pedestrians in the Wild
  2020/11/1
• StrObe: Streaming Object Detection from LiDAR Packets
  2020/11/1
• Perceive, Attend, and Drive: Learning Spatial Attention for Safe Self-Driving
  2020/11/1
• LiRaNet: End-to-End Trajectory Prediction using Spatio-Temporal Radar Fusion
  2020/10/1
• Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations
  2020/8/1
• End-to-end Contextual Perception and Prediction with Interaction Transformer
  2020/8/1
• Testing the Safety of Self-driving Vehicles by Simulating Perception and Prediction
  2020/8/1
• Implicit Latent Variable Model for Scene-Consistent Motion Forecasting
  2020/7/1
• Multi-Agent Routing Value Iteration Network
  2020/7/1
• The Importance of Prior Knowledge in Precise Multimodal Prediction
  2020/6/1
• PnPNet: End-to-End Perception and Prediction with Tracking in the Loop
  2020/5/1
• Physically Realizable Adversarial Examples for LiDAR Object Detection
  2020/4/1
• Identifying Unknown Instances for Autonomous Driving
  2019/10/1
• Jointly Learnable Behavior and Trajectory Planning for Self-Driving Vehicles
  2019/10/1
• Spatially-Aware Graph Neural Networks for Relational Behavior Forecasting from Sensor Data
  2019/10/1
• Discrete Residual Flow for Probabilistic Pedestrian Behavior Prediction
  2019/10/1
• Exploiting Sparse Semantic HD Maps for Self-Driving Vehicle Localization
  2019/8/1
• Deep Rigid Instance Scene Flow
  2019/4/1
• End-to-end Learning of Multi-sensor 3D Tracking by Detection
  2018/6/1
• MultiNet: Real-time Joint Semantic Reasoning for Autonomous Driving
  2016/12/1