3 citations · 11 across the 18 of their papers we have counts for
20 papers
Pantheon360: Taming Digital Twin Generation via 3D-Aware 360° Video Diffusion
Ting-Hsuan Chen, Ying-Huan Chen, Tao Tu +10
Generating complete digital twins from videos requires precise camera control, global scene coverage, and strict spatial-temporal consistency constraints that remain challenging fo…
UniDAC: Universal Metric Depth Estimation for Any Camera
Girish Chandar Ganesan, Yuliang Guo, Liu Ren +1
Monocular metric depth estimation (MMDE) is a core challenge in computer vision, playing a pivotal role in real-world applications that demand accurate spatial understanding. Altho…
No Calibration, No Depth, No Problem: Cross-Sensor View Synthesis with 3D Consistency
Cho-Ying Wu, Zixun Huang, Xinyu Huang +1
We present the first study of cross-sensor view synthesis across different modalities. We examine a practical, fundamental, yet widely overlooked problem: getting aligned RGB-X dat…
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…
AdaOcc: Adaptive-Resolution Occupancy Prediction
Chao Chen, Ruoyu Wang, Yuliang Guo +4
Autonomous driving in complex urban scenarios requires 3D perception to be both comprehensive and precise. Traditional 3D perception methods focus on object detection, resulting in…
Enhancing Online Road Network Perception and Reasoning with Standard Definition Maps
Hengyuan Zhang, David Paz, Yuliang Guo +5
Autonomous driving for urban and highway driving applications often requires High Definition (HD) maps to generate a navigation plan. Nevertheless, various challenges arise when ge…
Behind the Veil: Enhanced Indoor 3D Scene Reconstruction with Occluded Surfaces Completion
Su Sun, Cheng Zhao, Yuliang Guo +4
In this paper, we present a novel indoor 3D reconstruction method with occluded surface completion, given a sequence of depth readings. Prior state-of-the-art (SOTA) methods only f…
TCLC-GS: Tightly Coupled LiDAR-Camera Gaussian Splatting for Autonomous Driving
Cheng Zhao, Su Sun, Ruoyu Wang +6
Most 3D Gaussian Splatting (3D-GS) based methods for urban scenes initialize 3D Gaussians directly with 3D LiDAR points, which not only underutilizes LiDAR data capabilities but al…
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large Objects
Abhinav Kumar, Yuliang Guo, Xinyu Huang +2
Monocular 3D detectors achieve remarkable performance on cars and smaller objects. However, their performance drops on larger objects, leading to fatal accidents. Some attribute th…
SUP-NeRF: A Streamlined Unification of Pose Estimation and NeRF for Monocular 3D Object Reconstruction
Yuliang Guo, Abhinav Kumar, Cheng Zhao +3
Monocular 3D reconstruction for categorical objects heavily relies on accurately perceiving each object's pose. While gradient-based optimization in a NeRF framework updates the in…
3D Copy-Paste: Physically Plausible Object Insertion for Monocular 3D Detection
Yunhao Ge, Hong-Xing Yu, Cheng Zhao +5
A major challenge in monocular 3D object detection is the limited diversity and quantity of objects in real datasets. While augmenting real scenes with virtual objects holds promis…
Symmetry and Uncertainty-Aware Object SLAM for 6DoF Object Pose Estimation
Nathaniel Merrill, Yuliang Guo, Xingxing Zuo +5
We propose a keypoint-based object-level SLAM framework that can provide globally consistent 6DoF pose estimates for symmetric and asymmetric objects alike. To the best of our know…
OmniFusion: 360 Monocular Depth Estimation via Geometry-Aware Fusion
Yuyan Li, Yuliang Guo, Zhixin Yan +3
A well-known challenge in applying deep-learning methods to omnidirectional images is spherical distortion. In dense regression tasks such as depth estimation, where structural det…