computer-vision

Viewpoint Estimation

3260 papers • 126 benchmarks • 313 datasets

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Benchmarks

These leaderboards are used to track progress in viewpoint-estimation

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Dataset

Best Model

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Libraries

i

Use these libraries to find viewpoint-estimation models and implementations

Datasets

PASCAL3D+

Subtasks

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Most implemented papers

Render for CNN: Viewpoint Estimation in Images Using CNNs Trained with Rendered 3D Model Views

C. Qi, Hao Su, L. Guibas, Yangyan Li•Wed May 20 2015

A scalable and overfit-resistant image synthesis pipeline, together with a novel CNN specifically tailored for the viewpoint estimation task, is proposed that can significantly outperform state-of-the-art methods on PASCAL 3D+ benchmark.

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iSPA-Net: Iterative Semantic Pose Alignment Network

R. Venkatesh Babu, Jogendra Nath Kundu, Aditya Ganeshan, V. RahulM., Aditya Prakash•Thu Aug 02 2018

This work proposes an iterative Semantic Pose Alignment Network, called iSPA-Net, which focuses on exploiting semantic 3D structural regularity to solve the task of fine-grained pose estimation by predicting viewpoint difference between a given pair of images.

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3D Bounding Box Estimation Using Deep Learning and Geometry

J. Kosecka, A. Mousavian, John Flynn, Dragomir Anguelov•Wed Nov 30 2016

Although conceptually simple, this method outperforms more complex and computationally expensive approaches that leverage semantic segmentation, instance level segmentation and flat ground priors and produces state of the art results for 3D viewpoint estimation on the Pascal 3D+ dataset.

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Spherical Regression: Learning Viewpoints, Surface Normals and 3D Rotations on N-Spheres

Shuai Liao, E. Gavves, Cees G. M. Snoek•Tue Apr 09 2019

By introducing a spherical exponential mapping on n-spheres at the regression output, this work obtains well-behaved gradients, leading to stable training and shows how the spherical regression can be utilized for several computer vision challenges, specifically viewpoint estimation, surface normal estimation and 3D rotation estimation.

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When and how CNNs generalize to out-of-distribution category-viewpoint combinations

H. Pfister, F. Durand, Spandan Madan, Timothy M. Henry, Jamell Dozier, Helen Ho, Nishchal Bhandari, Tomotake Sasaki, X. Boix•Tue Jul 14 2020

It is shown that increasing the number of in-distribution combinations substantially improves generalization to OOD combinations, even with the same amount of training data, and it is demonstrated that such OOD generalization is facilitated by the neural mechanism of specialization.

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Few-Shot Object Detection and Viewpoint Estimation for Objects in the Wild

V. Lepetit, Renaud Marlet, Yang Xiao•Wed Jul 22 2020

This paper tackles the problems of few-shot object detection and few- shot viewpoint estimation, and introduces a simple category-agnostic viewpoint estimation method that outperforms state-of-the-art methods by a large margin on a range of datasets.

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Pose from Shape: Deep Pose Estimation for Arbitrary 3D Objects

Renaud Marlet, Mathieu Aubry, Yang Xiao, Xuchong Qiu, Pierre-Alain Langlois•Tue Jun 11 2019

This work proposes a completely generic deep pose estimation approach, which does not require the network to have been trained on relevant categories, nor objects in a category to have a canonical pose, and demonstrates that this method boosts performances for supervised category pose estimation on standard benchmarks.

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Adviser Networks: Learning What Question to Ask for Human-In-The-Loop Viewpoint Estimation

Jason J. Corso, Mohamed El Banani•Sun Feb 04 2018

This work forms a solution to the adviser problem using a deep network and applies it to the viewpoint estimation problem where the question asks for the location of a specific keypoint in the input image, and is able to outperform the previous hybrid-intelligence state-of-the-art.

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Object Pose Estimation from Monocular Image using Multi-View Keypoint Correspondence

R. Venkatesh Babu, Jogendra Nath Kundu, Aditya Ganeshan, V. RahulM.•Sun Sep 02 2018

This work proposes a data-efficient method which utilizes the geometric regularity of intraclass objects for pose estimation, which enables this method to achieve state-of-the-art performance on multiple real-image viewpoint estimation datasets, such as Pascal3D+ and ObjectNet3D.

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Self-Supervised Viewpoint Learning From Image Collections

C. Rother, Siva Karthik Mustikovela, V. Jampani, Sifei Liu, Jan Kautz, Shalini De Mello•Thu Apr 02 2020

This work proposes a novel learning framework which incorporates an analysis-by-synthesis paradigm to reconstruct images in a viewpoint aware manner with a generative network, along with symmetry and adversarial constraints to successfully supervise the authors' viewpoint estimation network.

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