3 citations · 11 across the 18 of their papers we have counts for
8 papers · 1 filter
NexusFlow: Unifying Disparate Tasks under Partial Supervision via Invertible Flow Networks
Fangzhou Lin, Yuping Wang, Yuliang Guo +7
Partially Supervised Multi-Task Learning (PS-MTL) aims to leverage knowledge across tasks when annotations are incomplete. Existing approaches, however, have largely focused on the…
Dino-Diffusion Modular Designs Bridge the Cross-Domain Gap in Autonomous Parking
Zixuan Wu, Hengyuan Zhang, Ting-Hsuan Chen +4
Parking is a critical pillar of driving safety. While recent end-to-end (E2E) approaches have achieved promising in-domain results, robustness under domain shifts (e.g., weather an…
CHARM3R: Towards Unseen Camera Height Robust Monocular 3D Detector
Abhinav Kumar, Yuliang Guo, Zhihao Zhang +3
Monocular 3D object detectors, while effective on data from one ego camera height, struggle with unseen or out-of-distribution camera heights. Existing methods often rely on Plucke…
3DGEER: 3D Gaussian Rendering Made Exact and Efficient for Generic Cameras
Zixun Huang, Cho-Ying Wu, Yuliang Guo +2
3D Gaussian Splatting (3DGS) achieves an appealing balance between rendering quality and efficiency, but relies on approximating 3D Gaussians as 2D projections--an assumption that…
Online Language Splatting
Saimouli Katragadda, Cho-Ying Wu, Yuliang Guo +3
To enable AI agents to interact seamlessly with both humans and 3D environments, they must not only perceive the 3D world accurately but also align human language with 3D spatial r…
SMART: Advancing Scalable Map Priors for Driving Topology Reasoning
Junjie Ye, David Paz, Hengyuan Zhang +5
Topology reasoning is crucial for autonomous driving as it enables comprehensive understanding of connectivity and relationships between lanes and traffic elements. While recent ap…
MapGS: Generalizable Pretraining and Data Augmentation for Online Mapping via Novel View Synthesis
Hengyuan Zhang, David Paz, Yuliang Guo +3
Online mapping reduces the reliance of autonomous vehicles on high-definition (HD) maps, significantly enhancing scalability. However, recent advancements often overlook cross-sens…
Depth Any Camera: Zero-Shot Metric Depth Estimation from Any Camera
Yuliang Guo, Sparsh Garg, S. Mahdi H. Miangoleh +2
While recent depth foundation models exhibit strong zero-shot generalization, achieving accurate metric depth across diverse camera types-particularly those with large fields of vi…