Search Results for author: Adarsh Kowdle

Found 11 papers, 4 papers with code

HITNet: Hierarchical Iterative Tile Refinement Network for Real-time Stereo Matching

6 code implementations CVPR 2021 Vladimir Tankovich, Christian Häne, yinda zhang, Adarsh Kowdle, Sean Fanello, Sofien Bouaziz

Contrary to many recent neural network approaches that operate on a full cost volume and rely on 3D convolutions, our approach does not explicitly build a volume and instead relies on a fast multi-resolution initialization step, differentiable 2D geometric propagation and warping mechanisms to infer disparity hypotheses.

Stereo Depth Estimation Stereo Disparity Estimation +1

Multimodal active speaker detection and virtual cinematography for video conferencing

no code implementations10 Feb 2020 Ross Cutler, Ramin Mehran, Sam Johnson, Cha Zhang, Adam Kirk, Oliver Whyte, Adarsh Kowdle

Active speaker detection (ASD) and virtual cinematography (VC) can significantly improve the remote user experience of a video conference by automatically panning, tilting and zooming of a video conferencing camera: users subjectively rate an expert video cinematographer's video significantly higher than unedited video.

LookinGood: Enhancing Performance Capture with Real-time Neural Re-Rendering

no code implementations12 Nov 2018 Ricardo Martin-Brualla, Rohit Pandey, Shuoran Yang, Pavel Pidlypenskyi, Jonathan Taylor, Julien Valentin, Sameh Khamis, Philip Davidson, Anastasia Tkach, Peter Lincoln, Adarsh Kowdle, Christoph Rhemann, Dan B. Goldman, Cem Keskin, Steve Seitz, Shahram Izadi, Sean Fanello

We take the novel approach to augment such real-time performance capture systems with a deep architecture that takes a rendering from an arbitrary viewpoint, and jointly performs completion, super resolution, and denoising of the imagery in real-time.

Denoising Super-Resolution +1

StereoNet: Guided Hierarchical Refinement for Real-Time Edge-Aware Depth Prediction

2 code implementations ECCV 2018 Sameh Khamis, Sean Fanello, Christoph Rhemann, Adarsh Kowdle, Julien Valentin, Shahram Izadi

A first estimate of the disparity is computed in a very low resolution cost volume, then hierarchically the model re-introduces high-frequency details through a learned upsampling function that uses compact pixel-to-pixel refinement networks.

Quantization Stereo Depth Estimation +2

Low Compute and Fully Parallel Computer Vision With HashMatch

no code implementations ICCV 2017 Sean Ryan Fanello, Julien Valentin, Adarsh Kowdle, Christoph Rhemann, Vladimir Tankovich, Carlo Ciliberto, Philip Davidson, Shahram Izadi

Numerous computer vision problems such as stereo depth estimation, object-class segmentation and foreground/background segmentation can be formulated as per-pixel image labeling tasks.

Disparity Estimation Image Retrieval +1

Revisiting Depth Layers from Occlusions

no code implementations CVPR 2013 Adarsh Kowdle, Andrew Gallagher, Tsuhan Chen

We cast the problem of depth-layer segmentation as a discrete labeling problem on a spatiotemporal Markov Random Field (MRF) that uses the motion occlusion cues along with monocular cues and a smooth motion prior for the moving object.

Towards Holistic Scene Understanding: Feedback Enabled Cascaded Classification Models

no code implementations NeurIPS 2010 Cong-Cong Li, Adarsh Kowdle, Ashutosh Saxena, Tsuhan Chen

In many machine learning domains (such as scene understanding), several related sub-tasks (such as scene categorization, depth estimation, object detection) operate on the same raw data and provide correlated outputs.

Classification Depth Estimation +6

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