ECCV
Collection
Accepted papers for ECCV (European Conference on Computer Vision), one dataset per year. • 4 items • Updated
paper_id uint32 0 1.64k | title stringlengths 15 155 | authors listlengths 1 18 | ecva_url stringlengths 76 79 | pdf_url stringlengths 70 70 | supp_url stringlengths 75 75 ⌀ | doi stringlengths 25 28 | arxiv_id stringlengths 10 10 ⌀ | arxiv_id_source stringclasses 2
values | abstract large_stringlengths 402 2.1k | embedding listlengths 768 768 |
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0 | Learning Depth from Focus in the Wild | [
"Changyeon Won",
"Hae-Gon Jeon"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/19_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610001.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610001-supp.pdf | 10.1007/978-3-031-19769-7_1 | 2207.09658 | title_snapshot | For better photography, most recent commercial cameras including smartphones have either adopted large-aperture lens to collect more light or used a burst mode to take multiple images within short times. These interesting features lead us to examine depth from focus/defocus. In this work, we present a convolutional neu... | [
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1 | Learning-Based Point Cloud Registration for 6D Object Pose Estimation in the Real World | [
"Zheng Dang",
"Lizhou Wang",
"Yu Guo",
"Mathieu Salzmann"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/69_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610018.pdf | null | 10.1007/978-3-031-19769-7_2 | 2203.15309 | title_judge | In this work, we tackle the task of estimating the 6D pose of an object from point cloud data. While recent learning-based approaches to addressing this task have shown great success on synthetic datasets, we have observed them to fail in the presence of real-world data. We thus analyze the causes of these failures, wh... | [
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2 | An End-to-End Transformer Model for Crowd Localization | [
"Dingkang Liang",
"Wei Xu",
"Xiang Bai"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/127_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610037.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610037-supp.pdf | 10.1007/978-3-031-19769-7_3 | 2202.13065 | title_snapshot | Crowd localization, predicting head positions, is a more practical and high-level task than simply counting. Existing methods employ pseudo-bounding boxes or pre-designed localization maps, relying on complex post-processing to obtain the head positions. In this paper, we propose an elegant, end-to-end Crowd Localizati... | [
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3 | Few-Shot Single-View 3D Reconstruction with Memory Prior Contrastive Network | [
"Zhen Xing",
"Yijiang Chen",
"Zhixin Ling",
"Xiangdong Zhou",
"Yu Xiang"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/192_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610054.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610054-supp.pdf | 10.1007/978-3-031-19769-7_4 | 2208.00183 | title_snapshot | 3D reconstruction of novel categories based on few-shot learning is appealing in real-world applications and attracts increasing research interests. Previous approaches mainly focus on how to design shape prior models for different categories. Their performance on unseen categories is not very competitive. In this pape... | [
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4 | DID-M3D: Decoupling Instance Depth for Monocular 3D Object Detection | [
"Liang Peng",
"Xiaopei Wu",
"Zheng Yang",
"Haifeng Liu",
"Deng Cai"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/343_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610071.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610071-supp.pdf | 10.1007/978-3-031-19769-7_5 | 2207.08531 | title_snapshot | Monocular 3D detection has drawn much attention from the community due to its low cost and setup simplicity. It takes an RGB image as input and predicts 3D boxes in the 3D space. The most challenging sub-task lies in the instance depth estimation. Previous works usually use a direct estimation method. However, in this ... | [
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5 | Adaptive Co-Teaching for Unsupervised Monocular Depth Estimation | [
"Weisong Ren",
"Lijun Wang",
"Yongri Piao",
"Miao Zhang",
"Huchuan Lu",
"Ting Liu"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/405_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610089.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610089-supp.pdf | 10.1007/978-3-031-19769-7_6 | null | null | Unsupervised depth estimation using photometric losses suffers from local minimum and training instability. We address this issue by proposing an adaptive co-teaching framework to distill the learned knowledge from unsupervised teacher networks to a student network. We design an ensemble architecture for our teacher ne... | [
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6 | Fusing Local Similarities for Retrieval-Based 3D Orientation Estimation of Unseen Objects | [
"Chen Zhao",
"Yinlin Hu",
"Mathieu Salzmann"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/444_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610106.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610106-supp.pdf | 10.1007/978-3-031-19769-7_7 | 2203.08472 | title_snapshot | In this paper, we tackle the task of estimating the 3D orientation of previously-unseen objects from monocular images. This task contrasts with the one considered by most existing deep learning methods which typically assume that the testing objects have been observed during training. To handle the unseen objects, we f... | [
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7 | Lidar Point Cloud Guided Monocular 3D Object Detection | [
"Liang Peng",
"Fei Liu",
"Zhengxu Yu",
"Senbo Yan",
"Dan Deng",
"Zheng Yang",
"Haifeng Liu",
"Deng Cai"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/655_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610123.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610123-supp.pdf | 10.1007/978-3-031-19769-7_8 | 2104.09035 | title_snapshot | Monocular 3D object detection is a challenging task in the self-driving and computer vision community. As a common practice, most previous works use manually annotated 3D box labels, where the annotating process is expensive. In this paper, we find that the precisely and carefully annotated labels may be unnecessary in... | [
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8 | Structural Causal 3D Reconstruction | [
"Weiyang Liu",
"Zhen Liu",
"Liam Paull",
"Adrian Weller",
"Bernhard Schölkopf"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/656_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610140.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610140-supp.pdf | 10.1007/978-3-031-19769-7_9 | 2207.10156 | title_snapshot | This paper considers the problem of unsupervised 3D object reconstruction from in-the-wild single-view images. Due to ambiguity and intrinsic ill-posedness, this problem is inherently difficult to solve and therefore requires strong regularization to achieve disentanglement of different latent factors. Unlike existing ... | [
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9 | 3D Human Pose Estimation Using Möbius Graph Convolutional Networks | [
"Niloofar Azizi",
"Horst Possegger",
"Emanuele Rodolà",
"Horst Bischof"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/1049_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610158.pdf | null | 10.1007/978-3-031-19769-7_10 | 2203.10554 | title_snapshot | 3D human pose estimation is fundamental to understanding human behavior. Recently, promising results have been achieved by graph convolutional networks(GCNs), which achieve state-of-the-art performance and provide rather light-weight architectures. However, a major limitation of GCNs is their inability to encode all th... | [
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10 | Learning to Train a Point Cloud Reconstruction Network without Matching | [
"Tianxin Huang",
"Xuemeng Yang",
"Jiangning Zhang",
"Jinhao Cui",
"Hao Zou",
"Jun Chen",
"Xiangrui Zhao",
"Yong Liu"
] | https://www.ecva.net/papers/eccv_2022/papers_ECCV/html/1235_ECCV_2022_paper.php | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610177.pdf | https://www.ecva.net/papers/eccv_2022/papers_ECCV/papers/136610177-supp.pdf | 10.1007/978-3-031-19769-7_11 | null | null | Reconstruction networks for well-ordered data such as 2D images and 1D continuous signals are easy to optimize through element-wised squared errors, while permutation-arbitrary point clouds cannot be constrained directly because their points permutations are not fixed. Though existing works design algorithms to match t... | [
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