Beyond PASCAL: A benchmark for 3D object detection in the wild

Xiang, Yu; Mottaghi, Roozbeh; Savarese, Silvio

doi:10.1109/wacv.2014.6836101

Cited by 687 publications

(816 citation statements)

References 19 publications

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“…With humanannotated part segments, we measured part matching accuracy using the weighted intersection over union (IoU) score between transferred segments and ground truths, with weights determined by the pixel area of each part. To evaluate alignment accuracy, we measured the PCK metric using keypoint annotations for the 12 rigid PASCAL classes [48]. Table 5 summarizes the matching accuracy compared to state-of-the-art correspondence methods.…”

Section: Resultsmentioning

confidence: 99%

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

Kim

Min

Ham

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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We present a descriptor, called fully convolutional selfsimilarity (FCSS), for dense semantic correspondence. To robustly match points among different instances within the same object class, we formulate FCSS using local selfsimilarity (LSS) within a fully convolutional network. In contrast to existing CNN-based descriptors, FCSS is inherently insensitive to intra-class appearance variations because of its LSS-based structure, while maintaining the precise localization ability of deep neural networks. The sampling patterns of local structure and the self-similarity measure are jointly learned within the proposed network in an end-to-end and multi-scale manner. As training data for semantic correspondence is rather limited, we propose to leverage object candidate priors provided in existing image datasets and also correspondence consistency between object pairs to enable weakly-supervised learning. Experiments demonstrate that FCSS outperforms conventional handcrafted descriptors and CNN-based descriptors on various benchmarks.

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Section: Resultsmentioning

confidence: 99%

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

Kim

Min

Ham

et al. 2019

IEEE Trans. Pattern Anal. Mach. Intell.

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“…To the best of our knowledge, our work is the first one that does 3D object pose regression using CNNs with axisangle/quaternion representations and geodesic loss functions, and shows good performance on a challenging dataset like Pascal3D+ [22]. We also note that 3D pose regression is commonly used in human pose estimation, to regress the joint locations of the human skeleton.…”

Section: Introductionmentioning

confidence: 85%

“…For our experiments, we use the Pascal 3D+ dataset (release 1.1) [22], which has 3D pose annotations for 12 common categories of interest: aeroplane (aero), bicycle (bike), boat, bottle, bus, car, chair, diningtable (dtable), motorbike (mbike), sofa, train, and tvmonitor (tv). The annotations are available for both VOC 2012 [1] and ImageNet [6] images.…”

Section: Datasetmentioning

confidence: 99%

3D Pose Regression Using Convolutional Neural Networks

Mahendran

Ali

Vidal

2017

2017 IEEE International Conference on Computer Vision Workshops (ICCVW)

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Abstract3D pose estimation is a key component of many important computer vision tasks such as autonomous navigation and 3D scene understanding. Most state-of-the-art approaches to 3D pose estimation solve this problem as a pose-classification problem in which the pose space is discretized into bins and a CNN classifier is used to predict a pose bin. We argue that the 3D pose space is continuous and propose to solve the pose estimation problem in a CNN regression framework with a suitable representation, data augmentation and loss function that captures the geometry of the pose space. Experiments on PASCAL3D+ show that the proposed 3D pose regression approach achieves competitive performance compared to the state-of-the-art.

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“…Using this fine differentiation, we can achieve finer pose estimation. There are a few recent papers [27,36,29] that have created a dataset for real images annotated with full 3D poses.…”

Section: Related Workmentioning

confidence: 99%

FPM: Fine Pose Parts-Based Model with 3D CAD Models

Lim

Khosla

Torralba

2014

Computer Vision – ECCV 2014

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Abstract. We introduce a novel approach to the problem of localizing objects in an image and estimating their fine-pose. Given exact CAD models, and a few real training images with aligned models, we propose to leverage the geometric information from CAD models and appearance information from real images to learn a model that can accurately estimate fine pose in real images. Specifically, we propose FPM, a fine pose parts-based model, that combines geometric information in the form of shared 3D parts in deformable part based models, and appearance information in the form of objectness to achieve both fast and accurate fine pose estimation. Our method significantly outperforms current state-ofthe-art algorithms in both accuracy and speed.

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Beyond PASCAL: A benchmark for 3D object detection in the wild

Cited by 687 publications

References 19 publications

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

FCSS: Fully Convolutional Self-Similarity for Dense Semantic Correspondence

3D Pose Regression Using Convolutional Neural Networks

FPM: Fine Pose Parts-Based Model with 3D CAD Models

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