4 citations · 4 across the 7 of their papers we have counts for
7 papers
Adapting Vision Foundation Models to Acoustics for Pose-Free 3D Sonar Reconstruction
Kevin Zhang, Jingxi Chen, Mohamad Qadri +5
Vision foundation models trained on Internet-scale RGB datasets enable remarkable capabilities across a range of tasks, from text-to-video generation to few-shot 3D scene reconstru…
Underwater Monocular Metric Depth Estimation: Real-World Benchmarks and Synthetic Fine-Tuning with Vision Foundation Models
Zijie Cai, Christopher Metzler
Monocular depth estimation has recently progressed beyond ordinal depth to provide metric depth predictions. However, its reliability in underwater environments remains limited due…
Acoustic Neural 3D Reconstruction Under Pose Drift
Tianxiang Lin, Mohamad Qadri, Kevin Zhang +3
We consider the problem of optimizing neural implicit surfaces for 3D reconstruction using acoustic images collected with drifting sensor poses. The accuracy of current state-of-th…
Z-Splat: Z-Axis Gaussian Splatting for Camera-Sonar Fusion
Ziyuan Qu, Omkar Vengurlekar, Mohamad Qadri +5
Differentiable 3D-Gaussian splatting (GS) is emerging as a prominent technique in computer vision and graphics for reconstructing 3D scenes. GS represents a scene as a set of 3D Ga…
AONeuS: A Neural Rendering Framework for Acoustic-Optical Sensor Fusion
Mohamad Qadri, Kevin Zhang, Akshay Hinduja +3
Underwater perception and 3D surface reconstruction are challenging problems with broad applications in construction, security, marine archaeology, and environmental monitoring. Tr…
ConVRT: Consistent Video Restoration Through Turbulence with Test-time Optimization of Neural Video Representations
Haoming Cai, Jingxi Chen, Brandon Y. Feng +5
tmospheric turbulence presents a significant challenge in long-range imaging. Current restoration algorithms often struggle with temporal inconsistency, as well as limited generali…
A Scalable Training Strategy for Blind Multi-Distribution Noise Removal
Kevin Zhang, Sakshum Kulshrestha, Christopher Metzler
Despite recent advances, developing general-purpose universal denoising and artifact-removal networks remains largely an open problem: Given fixed network weights, one inherently t…