Search Results for author: David W. Jacobs

Found 19 papers, 3 papers with code

Autoregressive Perturbations for Data Poisoning

1 code implementation8 Jun 2022 Pedro Sandoval-Segura, Vasu Singla, Jonas Geiping, Micah Goldblum, Tom Goldstein, David W. Jacobs

Unfortunately, existing methods require knowledge of both the target architecture and the complete dataset so that a surrogate network can be trained, the parameters of which are used to generate the attack.

Data Poisoning

Fast Light-Weight Near-Field Photometric Stereo

no code implementations CVPR 2022 Daniel Lichy, Soumyadip Sengupta, David W. Jacobs

Existing approaches rely on optimization coupled with a far-field PS network operating on pixels or small patches.

Shape and Material Capture at Home

1 code implementation CVPR 2021 Daniel Lichy, Jiaye Wu, Soumyadip Sengupta, David W. Jacobs

In this paper, we present a technique for estimating the geometry and reflectance of objects using only a camera, flashlight, and optionally a tripod.

Neural Inverse Rendering of an Indoor Scene from a Single Image

no code implementations ICCV 2019 Soumyadip Sengupta, Jinwei Gu, Kihwan Kim, Guilin Liu, David W. Jacobs, Jan Kautz

Inverse rendering aims to estimate physical attributes of a scene, e. g., reflectance, geometry, and lighting, from image(s).

Self-Supervised Learning

End-to-end Recovery of Human Shape and Pose

7 code implementations CVPR 2018 Angjoo Kanazawa, Michael J. Black, David W. Jacobs, Jitendra Malik

The main objective is to minimize the reprojection loss of keypoints, which allow our model to be trained using images in-the-wild that only have ground truth 2D annotations.

3D Hand Pose Estimation 3D Human Shape Estimation +4

Seeing What Is Not There: Learning Context to Determine Where Objects Are Missing

no code implementations CVPR 2017 Jin Sun, David W. Jacobs

Combined with object detection results, we can perform a novel vision task: finding where objects are missing in an image.

object-detection Object Detection

A New Rank Constraint on Multi-view Fundamental Matrices, and its Application to Camera Location Recovery

no code implementations CVPR 2017 Soumyadip Sengupta, Tal Amir, Meirav Galun, Tom Goldstein, David W. Jacobs, Amit Singer, Ronen Basri

We show that in general, with the selection of proper scale factors, a matrix formed by stacking fundamental matrices between pairs of images has rank 6.

WarpNet: Weakly Supervised Matching for Single-view Reconstruction

no code implementations CVPR 2016 Angjoo Kanazawa, David W. Jacobs, Manmohan Chandraker

This is in contrast to prior works that require part annotations, since matching objects across class and pose variations is challenging with appearance features alone.

Learning 3D Deformation of Animals from 2D Images

no code implementations28 Jul 2015 Angjoo Kanazawa, Shahar Kovalsky, Ronen Basri, David W. Jacobs

In this paper, we show that such information can be learned from user-clicked 2D images and a template 3D model of the target animal.

Deep Hierarchical Parsing for Semantic Segmentation

no code implementations CVPR 2015 Abhishek Sharma, Oncel Tuzel, David W. Jacobs

We propose to tackle this problem by including the classification loss of the internal nodes of the random parse trees in the original RCPN loss function.

General Classification Scene Parsing +1

Riemannian Metric Learning for Symmetric Positive Definite Matrices

no code implementations10 Jan 2015 Raviteja Vemulapalli, David W. Jacobs

In this work, we focus on the log-Euclidean Riemannian geometry and propose a data-driven approach for learning Riemannian metrics/geodesic distances for SPD matrices.

Metric Learning

From Shading to Local Shape

no code implementations10 Oct 2013 Ying Xiong, Ayan Chakrabarti, Ronen Basri, Steven J. Gortler, David W. Jacobs, Todd Zickler

We develop a framework for extracting a concise representation of the shape information available from diffuse shading in a small image patch.

Surface Reconstruction

Sparse Norm Filtering

no code implementations17 May 2013 Chengxi Ye, DaCheng Tao, Mingli Song, David W. Jacobs, Min Wu

Optimization-based filtering smoothes an image by minimizing a fidelity function and simultaneously preserves edges by exploiting a sparse norm penalty over gradients.

Colorization Deblurring +2

Spectral Graph Cut from a Filtering Point of View

no code implementations20 May 2012 Chengxi Ye, Yuxu Lin, Mingli Song, Chun Chen, David W. Jacobs

In this paper, we analyze image segmentation algorithms that are based on spectral graph theory, e. g., normalized cut, and show that there is a natural connection between spectural graph theory based image segmentationand and edge preserving filtering.

Image Segmentation Semantic Segmentation

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