4 citations · 6 across the 9 of their papers we have counts for
11 papers
FixAnything: 3D-Consistent Rendering Refinement via Video Generative Priors
Khiem Vuong, Deva Ramanan, Srinivasa Narasimhan
Rendering views using 3D scene representations such as Gaussian Splatting (3DGS), Neural Radiance Fields (NeRF), meshes, or even point clouds produces artifacts when input views ar…
Dash2Sim: Closed-Loop Driving Simulation from in-the-wild Dashcam Videos
Anurag Ghosh, Francesco Pittaluga, Khiem Vuong +4
Self-driving simulations typically rely on data collected in a small number of cities or on hand-authored synthetic scenarios. Dashcam videos cover a far broader range of locations…
FrameCrafter: Novel View Synthesis as Video Completion
Qi Wu, Khiem Vuong, Minsik Jeon +2
We tackle the problem of sparse novel view synthesis (NVS) using video diffusion models; given () multi-view images of a scene and their camera poses, we predict the…
AerialMegaDepth: Learning Aerial-Ground Reconstruction and View Synthesis
Khiem Vuong, Anurag Ghosh, Deva Ramanan +2
We explore the task of geometric reconstruction of images captured from a mixture of ground and aerial views. Current state-of-the-art learning-based approaches fail to handle the…
ROADWork: A Dataset and Benchmark for Learning to Recognize, Observe, Analyze and Drive Through Work Zones
Anurag Ghosh, Shen Zheng, Robert Tamburo +7
Perceiving and autonomously navigating through work zones is a challenging and underexplored problem. Open datasets for this long-tailed scenario are scarce. We propose the ROADWor…
WALT3D: Generating Realistic Training Data from Time-Lapse Imagery for Reconstructing Dynamic Objects under Occlusion
Khiem Vuong, N. Dinesh Reddy, Robert Tamburo +1
Current methods for 2D and 3D object understanding struggle with severe occlusions in busy urban environments, partly due to the lack of large-scale labeled ground-truth annotation…
Toward Planet-Wide Traffic Camera Calibration
Khiem Vuong, Robert Tamburo, Srinivasa G. Narasimhan
Despite the widespread deployment of outdoor cameras, their potential for automated analysis remains largely untapped due, in part, to calibration challenges. The absence of precis…
Egocentric Scene Understanding via Multimodal Spatial Rectifier
Tien Do, Khiem Vuong, Hyun Soo Park
In this paper, we study a problem of egocentric scene understanding, i.e., predicting depths and surface normals from an egocentric image. Egocentric scene understanding poses unpr…
Deep Multi-view Depth Estimation with Predicted Uncertainty
Tong Ke, Tien Do, Khiem Vuong +2
In this paper, we address the problem of estimating dense depth from a sequence of images using deep neural networks. Specifically, we employ a dense-optical-flow network to comput…
Deep Depth Estimation from Visual-Inertial SLAM
Kourosh Sartipi, Tien Do, Tong Ke +2
This paper addresses the problem of learning to complete a scene's depth from sparse depth points and images of indoor scenes. Specifically, we study the case in which the sparse d…
Surface Normal Estimation of Tilted Images via Spatial Rectifier
Tien Do, Khiem Vuong, Stergios I. Roumeliotis +1
In this paper, we present a spatial rectifier to estimate surface normals of tilted images. Tilted images are of particular interest as more visual data are captured by arbitrarily…