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Zehao Zhu

7 accepted papers

2026

Sensor2Sensor: Cross-Embodiment Sensor Conversion for Autonomous Driving

CVPR 2026

Robust training and validation of Autonomous Driving Systems (ADS) require massive, diverse datasets. Proprietary data collected by Autonomous Vehicle (AV) fleets, while high-fidelity, are limited in scale, diversity of sensor configurations, as well as geographic and long-tail-behavioral coverage.

Cited by 0SourceScholar
2025

Drive&Gen: Co-Evaluating End-to-End Driving and Video Generation Models

IROS 2025

Recent advances in generative models have sparked exciting new possibilities in the field of autonomous vehicles. Specifically, video generation models are now being explored as controllable virtual testing environments. Simultaneously, end-to-end (E2E) driving models have emerged as a streamlined a

Cited by 0SourceScholar
2025

Indoor FireRescue Radar: 4D Indoor Millimeter Wave Dataset and Analysis for Hazardous Environment Perception

IROS 2025

Fire-induced indoor environments, characterized by smoke, glare, and dimness, critically challenge rescue safety. While LiDAR and cameras suffer from signal attenuation, millimeter-wave (mmWave) radar exhibits robust imaging performance. Radar-based building mapping and object detection in indoor en

Cited by 1SourceScholar
2025

SceneCrafter: Controllable Multi-View Driving Scene Editing

CVPR 2025poster

Simulation is crucial for developing and evaluating autonomous vehicle (AV) systems. Recent literature builds on a new generation of generative models to synthesize highly realistic images for full-stack simulation. However, purely synthetically generated scenes are not grounded in reality and have…

Cited by 0SourcePDFScholar
2024

FSGS: Real-Time Few-shot View Synthesis using Gaussian Splatting

ECCV 2024poster

"Novel view synthesis from limited observations remains a crucial and ongoing challenge. In the realm of NeRF-based few-shot view synthesis, there is often a trade-off between the accuracy of the synthesized view and the efficiency of the 3D representation. To tackle this dilemma, we introduce a Few…

2024

Feature 3DGS: Supercharging 3D Gaussian Splatting to Enable Distilled Feature Fields

CVPR 2024highlight

3D scene representations have gained immense popularity in recent years. Methods that use Neural Radiance fields are versatile for traditional tasks such as novel view synthesis. In recent times some work has emerged that aims to extend the functionality of NeRF beyond view synthesis for semanticall…

2024

LightGaussian: Unbounded 3D Gaussian Compression with 15x Reduction and 200+ FPS

NeurIPS 2024spotlight

Recent advances in real-time neural rendering using point-based techniques have enabled broader adoption of 3D representations. However, foundational approaches like 3D Gaussian Splatting impose substantial storage overhead, as Structure-from-Motion (SfM) points can grow to millions, often requiring…