Sergio Casas

papers

1,994

total citations

papers (19)

TrafficSim: Learning To Simulate Realistic Multi-Agent Behaviors

CVPR 2021arXiv

283

citations

MP3: A Unified Model To Map, Perceive, Predict and Plan

CVPR 2021arXiv

279

citations

Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations

ECCV 2020arXiv

226

citations

Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

ICLR 2024arXiv

citations

Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

CVPR 2021arXiv

citations

Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving

CVPR 2023arXiv

citations

UnO: Unsupervised Occupancy Fields for Perception and Forecasting

CVPR 2024arXiv

citations

Towards Unsupervised Object Detection From LiDAR Point Clouds

CVPR 2023arXiv

citations

MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory

ICCV 2023arXiv

citations

DeTra: A Unified Model for Object Detection and Trajectory Forecasting

ECCV 2024arXiv

citations

Learning to Drive via Asymmetric Self-Play

ECCV 2024arXiv

citations

DIO: Decomposable Implicit 4D Occupancy-Flow World Model

CVPR 2025

citations

MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation

CVPR 2023

citations

MAD: Memory-Augmented Detection of 3D Objects

CVPR 2025

citations

Sergio Casas

papers (19)

TrafficSim: Learning To Simulate Realistic Multi-Agent Behaviors

MP3: A Unified Model To Map, Perceive, Predict and Plan

Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations

PnPNet: End-to-End Perception and Prediction With Tracking in the Loop

AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving

UnO: Unsupervised Occupancy Fields for Perception and Forecasting

Towards Unsupervised Object Detection From LiDAR Point Clouds

MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory

DeTra: A Unified Model for Object Detection and Trajectory Forecasting

Learning to Drive via Asymmetric Self-Play

DIO: Decomposable Implicit 4D Occupancy-Flow World Model

MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation

MAD: Memory-Augmented Detection of 3D Objects

papers (19)

TrafficSim: Learning To Simulate Realistic Multi-Agent Behaviors

MP3: A Unified Model To Map, Perceive, Predict and Plan

Perceive, Predict, and Plan: Safe Motion Planning Through Interpretable Semantic Representations

PnPNet: End-to-End Perception and Prediction With Tracking in the Loop

AdvSim: Generating Safety-Critical Scenarios for Self-Driving Vehicles

Implicit Latent Variable Model for Scene-Consistent Motion Forecasting

LookOut: Diverse Multi-Future Prediction and Planning for Self-Driving

RadarNet: Exploiting Radar for Robust Perception of Dynamic Objects

Copilot4D: Learning Unsupervised World Models for Autonomous Driving via Discrete Diffusion

Deep Multi-Task Learning for Joint Localization, Perception, and Prediction

Implicit Occupancy Flow Fields for Perception and Prediction in Self-Driving

UnO: Unsupervised Occupancy Fields for Perception and Forecasting

Towards Unsupervised Object Detection From LiDAR Point Clouds

MemorySeg: Online LiDAR Semantic Segmentation with a Latent Memory

DeTra: A Unified Model for Object Detection and Trajectory Forecasting

Learning to Drive via Asymmetric Self-Play

DIO: Decomposable Implicit 4D Occupancy-Flow World Model

MixSim: A Hierarchical Framework for Mixed Reality Traffic Simulation

MAD: Memory-Augmented Detection of 3D Objects