Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset

Hua, Yiwen; Kohli, Puneet; Uplavikar, Pritish; Ravi, Anand; Gunaseelan, Saravana; Orozco, Jason; Li, Edward

doi:10.48550/arxiv.2003.11172

Cited by 2 publications

(2 citation statements)

References 41 publications

(64 reference statements)

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“…We demonstrate this by using the hyperparameter and trained weights from the Middlebury dataset and do inference on true in-the-wild datasets that lack ground-truth or knowledge about the camera intrinsics. To this end we test our framework on two different publicly available datasets, namely the Holopix50k dataset [36] and the Flickr1024 dataset [37]. As Fig.…”

Section: H Generalization Testmentioning

confidence: 99%

FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion

Hirner

Fraundorfer

2022

2022 26th International Conference on Pattern Recognition (ICPR)

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We propose an accurate and lightweight convolutional neural network for stereo estimation with depth completion. We name this method fully-convolutional deformable similarity network with depth completion (FCDSN-DC). This method extends FC-DCNN by improving the feature extractor, adding a network structure for training highly accurate similarity functions and a network structure for filling inconsistent disparity estimates. The whole method consists of three parts. The first part consists of fully-convolutional densely connected layers that computes expressive features of rectified image pairs. The second part of our network learns highly accurate similarity functions between this learned features. It consists of densely-connected convolution layers with a deformable convolution block at the end to further improve the accuracy of the results. After this step an initial disparity map is created and the left-right consistency check is performed in order to remove inconsistent points. The last part of the network then uses this input together with the corresponding left RGB image in order to train a network that fills in the missing measurements. Consistent depth estimations are gathered around invalid points and are parsed together with the RGB points into a shallow CNN network structure in order to recover the missing values. We evaluate our method on challenging real world indoor and outdoor scenes, in particular Middlebury, KITTI and ETH3D were it produces competitive results. We furthermore show that this method generalizes well and is well suited for many applications without the need of further training.

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Section: H Generalization Testmentioning

confidence: 99%

FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion

Hirner

Fraundorfer

2022

2022 26th International Conference on Pattern Recognition (ICPR)

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“…Unsupervised datasets. In addition, there are some datasets for unsupervised stereo matching, which only provide left and right view images and no ground truth, representative datasets include Flickr1024 [29], Holopix50k [30] and WSVD [31].…”

Section: A Stereo Matching Datasetmentioning

confidence: 99%

PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction

Wang¹,

Ma²,

Liu³

et al. 2021

Preprint

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Stereo matching is an important task in computer vision which has drawn tremendous research attention for decades. While in terms of disparity accuracy, density and data size, public stereo datasets are difficult to meet the requirements of models. In this paper, we aim to address the issue between datasets and models and propose a large scale stereo dataset with high accuracy disparity ground truth named PlantStereo. We used a semi-automatic way to construct the dataset: after camera calibration and image registration, high accuracy disparity images can be obtained from the depth images. In total, PlantStereo contains 812 image pairs covering a diverse set of plants: spinach, tomato, pepper and pumpkin. We firstly evaluated our PlantStereo dataset on four different stereo matching methods. Extensive experiments on different models and plants show that compared with ground truth in integer accuracy, high accuracy disparity images provided by PlantStereo can remarkably improve the training effect of deep learning models. This paper provided a feasible and reliable method to realize plant surface dense reconstruction. The PlantStereo dataset and relative code are available at: https://www.github.com/wangqingyu985/PlantStereo

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Holopix50k: A Large-Scale In-the-wild Stereo Image Dataset

Cited by 2 publications

References 41 publications

FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion

FCDSN-DC: An Accurate and Lightweight Convolutional Neural Network for Stereo Estimation with Depth Completion

PlantStereo: A Stereo Matching Benchmark for Plant Surface Dense Reconstruction

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