Search Results for author: Neal Wadhwa

Found 12 papers, 4 papers with code

Du²Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

no code implementations ECCV 2020 Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano, Christian Häne, Sean Fanello, Rahul Garg

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.

Depth Estimation Stereo Matching

RealFill: Reference-Driven Generation for Authentic Image Completion

no code implementations28 Sep 2023 Luming Tang, Nataniel Ruiz, Qinghao Chu, Yuanzhen Li, Aleksander Holynski, David E. Jacobs, Bharath Hariharan, Yael Pritch, Neal Wadhwa, Kfir Aberman, Michael Rubinstein

Once personalized, RealFill is able to complete a target image with visually compelling contents that are faithful to the original scene.

HyperDreamBooth: HyperNetworks for Fast Personalization of Text-to-Image Models

2 code implementations CVPR 2024 Nataniel Ruiz, Yuanzhen Li, Varun Jampani, Wei Wei, Tingbo Hou, Yael Pritch, Neal Wadhwa, Michael Rubinstein, Kfir Aberman

By composing these weights into the diffusion model, coupled with fast finetuning, HyperDreamBooth can generate a person's face in various contexts and styles, with high subject details while also preserving the model's crucial knowledge of diverse styles and semantic modifications.

Diffusion Personalization Tuning Free

Defocus Map Estimation and Deblurring from a Single Dual-Pixel Image

no code implementations ICCV 2021 Shumian Xin, Neal Wadhwa, Tianfan Xue, Jonathan T. Barron, Pratul P. Srinivasan, Jiawen Chen, Ioannis Gkioulekas, Rahul Garg

We use data captured with a consumer smartphone camera to demonstrate that, after a one-time calibration step, our approach improves upon prior works for both defocus map estimation and blur removal, despite being entirely unsupervised.

Deblurring

Zoom-to-Inpaint: Image Inpainting with High-Frequency Details

1 code implementation17 Dec 2020 Soo Ye Kim, Kfir Aberman, Nori Kanazawa, Rahul Garg, Neal Wadhwa, Huiwen Chang, Nikhil Karnad, Munchurl Kim, Orly Liba

Although deep learning has enabled a huge leap forward in image inpainting, current methods are often unable to synthesize realistic high-frequency details.

Image Inpainting Super-Resolution +1

Learned Dual-View Reflection Removal

no code implementations1 Oct 2020 Simon Niklaus, Xuaner Cecilia Zhang, Jonathan T. Barron, Neal Wadhwa, Rahul Garg, Feng Liu, Tianfan Xue

Traditional reflection removal algorithms either use a single image as input, which suffers from intrinsic ambiguities, or use multiple images from a moving camera, which is inconvenient for users.

Reflection Removal

Learning to Autofocus

no code implementations CVPR 2020 Charles Herrmann, Richard Strong Bowen, Neal Wadhwa, Rahul Garg, Qiurui He, Jonathan T. Barron, Ramin Zabih

Autofocus is an important task for digital cameras, yet current approaches often exhibit poor performance.

Depth Estimation

Du$^2$Net: Learning Depth Estimation from Dual-Cameras and Dual-Pixels

no code implementations31 Mar 2020 Yinda Zhang, Neal Wadhwa, Sergio Orts-Escolano, Christian Häne, Sean Fanello, Rahul Garg

Computational stereo has reached a high level of accuracy, but degrades in the presence of occlusions, repeated textures, and correspondence errors along edges.

Depth Estimation Stereo Matching

Learning Single Camera Depth Estimation using Dual-Pixels

1 code implementation ICCV 2019 Rahul Garg, Neal Wadhwa, Sameer Ansari, Jonathan T. Barron

Using our approach, existing monocular depth estimation techniques can be effectively applied to dual-pixel data, and much smaller models can be constructed that still infer high quality depth.

Monocular Depth Estimation

Wireless Software Synchronization of Multiple Distributed Cameras

no code implementations21 Dec 2018 Sameer Ansari, Neal Wadhwa, Rahul Garg, Jiawen Chen

We present a method for precisely time-synchronizing the capture of image sequences from a collection of smartphone cameras connected over WiFi.

Stereo Depth Estimation

Aperture Supervision for Monocular Depth Estimation

no code implementations CVPR 2018 Pratul P. Srinivasan, Rahul Garg, Neal Wadhwa, Ren Ng, Jonathan T. Barron

We present a novel method to train machine learning algorithms to estimate scene depths from a single image, by using the information provided by a camera's aperture as supervision.

Monocular Depth Estimation

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