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Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal Estimation for 3D Scene Understanding

Cross-Domain Synthetic-to-Real In-the-Wild Depth and Normal ...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Jay Bhanushali Manivannan Muniyandi Praneeth Chakravarthula Indian Institute of Technology Madras UNC Chapel Hill

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

We present a cross-domain inference technique that learns from synthetic data to estimate depth and normals for in-the-wild omnidirectional 3D scenes encountered in real-world uncontrolled settings. To this end, we introduce UBotNet, an architecture that combines UNet and Bottleneck Transformer elements to predict consistent scene normals and depth. We also introduce the OmniHorizon synthetic dataset containing 24,335 omnidirectional images that represent a wide variety of outdoor environments, including buildings, streets, and diverse vegetation. This dataset is generated from expansive, lifelike virtual spaces and encompasses dynamic scene elements, such as changing lighting conditions, different times of day, pedestrians, and vehicles. Our experiments show that UBotNet achieves significantly improved accuracy in depth estimation and normal estimation compared to existing models. Lastly, we validate cross-domain synthetic-to-real depth and normal estimation on real outdoor images using UBot-Net trained solely on our synthetic OmniHorizon dataset, demonstrating the potential of both the synthetic dataset and the proposed network for real-world scene understanding applications. The dataset and accompanying code are available at ***.

关键词： Space vehicles Three-dimensional displays Pedestrians Accuracy Estimation Vegetation mapping computer architecture

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Image-caption difficulty for efficient weakly-supervised object detection from in-the-wild data

Image-caption difficulty for efficient weakly-supervised obj...

引用

ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Giacomo Nebbia Adriana Kovashka University of Pittsburgh

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In recent years, we have witnessed the collection of larger and larger multi-modal, image-caption datasets: from hundreds of thousands such pairs to hundreds of millions. Such datasets allow researchers to build powerful deep learning models, at the cost of requiring intensive computational resources. In this work, we ask: can we use such datasets efficiently without sacrificing performance? We tackle this problem by extracting difficulty scores from each image-caption sample, and by using such scores to make training more effective and efficient. We compare two ways to use difficulty scores to influence training: filtering a representative subset of each dataset and ordering samples through curriculum learning. We analyze and compare difficulty scores extracted from a single modality—captions (i.e., caption length and number of object mentions) or images (i.e., region proposals’ size and number)—or based on alignment of image-caption pairs (i.e., CLIP and concreteness). We focus on Weakly-Supervised Object Detection where image-level labels are extracted from captions. We discover that (1) combining filtering and curriculum learning can achieve large gains in performance, but not all methods are stable across experimental settings, (2) singlemodality scores often outperform alignment-based ones, (3) alignment scores show the largest gains when training time is limited.

关键词： Training Deep learning computer vision Costs Filtering conferences Computational modeling

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3D Human Scan With A Moving Event Camera

3D Human Scan With A Moving Event Camera

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Kai Kohyama Shintaro Shiba Yoshimitsu Aoki Keio University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Capturing the 3D human body is one of the important tasks in computer vision with a wide range of applications such as virtual reality and sports analysis. However, conventional frame cameras are limited by their temporal resolution and dynamic range, which imposes constraints in real-world application setups. Event cameras have the advantages of high temporal resolution and high dynamic range (HDR), but the development of event-based methods is necessary to handle data with different characteristics. This paper proposes a novel event-based method for 3D pose estimation and human mesh recovery. Prior work on event-based human mesh recovery require frames (images) as well as event data. The proposed method solely relies on events; it carves 3D voxels by moving the event camera around a stationary body, reconstructs the human pose and mesh by attenuated rays, and fit statistical body models, preserving high-frequency details. The experimental results show that the proposed method outperforms conventional frame-based methods in the estimation accuracy of both pose and body mesh. We also demonstrate results in challenging situations where other frame-based methods suffer from motion blur. This is the first-of-its-kind to demonstrate event-only human mesh recovery, and we hope that it is the first step toward achieving robust and accurate 3D human body scanning from vision sensors.

关键词： computer vision Solid modeling Three-dimensional displays Accuracy Pose estimation Virtual reality vision sensors

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NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset, Methods and Results

NTIRE 2021 Challenge on High Dynamic Range Imaging: Dataset,...

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ieee/cvf conference on computer vision and pattern recognition (CVPR)

作者： Perez-Pellitero, Eduardo Catley-Chandar, Sibi Leonardis, Ales Timofte, Radu Wang, Xian Li, Yong Wang, Tao Song, Fenglong Liu, Zhen Lin, Wenjie Li, Xinpeng Rao, Qing Jiang, Ting Han, Mingyan Fan, Haoqiang Sun, Jian Liu, Shuaicheng Chen, Xiangyu Liu, Yihao Zhang, Zhengwen Qiao, Yu Dong, Chao Chee, Evelyn Yi Lyn Shen, Shanlan Duan, Yubo Chen, Guannan Sun, Mengdi Gao, Yan Zhang, Lijie Akhil, K. A. Jiji, C., V Sharif, S. M. A. Naqvi, Rizwan Ali Biswas, Mithun Kim, Sungjun Xia, Chenjie Zhao, Bowen Ye, Zhangyu Lu, Xiwen Cao, Yanpeng Yang, Jiangxin Cao, Yanlong Rosh, Green K. S. Lomte, Sachin Deepak Krishnan, Nikhil Prasad, B. H. Pawan Huawei Noahs Ark Lab Shenzhen Peoples R China Megvii Technol Beijing Peoples R China Chinese Acad Sci Shenzhen Inst Adv Technol Shenzhen Peoples R China Black Sesame Technol Singapore Singapore BOE Technol Co Ltd Beijing Peoples R China Coll Engn Trivandrum Trivandrum Kerala India Zhejiang Univ State Key Lab Fluid Power & Mechatron Syst Hangzhou Peoples R China Zhejiang Univ Key Lab Adv Mfg Technol Hangzhou Peoples R China Sejong Univ Seoul South Korea Rigel IT Dhaka Bangladesh Samsung R&D Inst India Bangalore SRI B Bangalore Karnataka India

ISBN: (纸本)9781665448994

This paper reviews the first challenge on high-dynamic range (HDR) imaging that was part of the New Trends in Image Restoration and Enhancement (NTIRE) workshop, held in conjunction with CVPR 2021. This manuscript focuses on the newly introduced dataset, the proposed methods and their results. The challenge aims at estimating a HDR image from one or multiple respective low-dynamic range (LDR) observations, which might suffer from underor over-exposed regions and different sources of noise. The challenge is composed by two tracks: In Track 1 only a single LDR image is provided as input, whereas in Track 2 three differently-exposed LDR images with inter-frame motion are available. In both tracks, the ultimate goal is to achieve the best objective HDR reconstruction in terms of PSNR with respect to a ground-truth image, evaluated both directly and with a canonical tonemapping operation.

关键词： computer vision Tracking conferences Imaging Dynamic range Market research pattern recognition

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Efficient local correlation volume for unsupervised optical flow estimation on small moving objects in large satellite images

Efficient local correlation volume for unsupervised optical ...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Sarra Khairi Etienne Meunier Renaud Fraisse Patrick Bouthemy Inria Rennes France Airbus Defence & Space Toulouse France

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

With the advent of deep learning methods, performance and efficiency of optical flow estimation has significantly increased, especially for supervised models. However, they do not generalize well to more specific data involving small moving objects in large images, such as high-resolution aerial or satellite sequences. In addition, annotation and realistic simulation are difficult for these contents, which calls for unsupervised alternatives. Yet, the latter are still less accurate than their supervised counterparts. In this paper, we introduce an unsupervised local optical flow estimation method adapted to small moving objects in large-size images by involving no downsampling of the feature maps. We adopt a local correlation search and implement it in an original way with a per-shift computation, which minimizes memory consumption and speed up inference computation for large-scale images. We also design a loss function combining similarity, smoothness and sparsity constraints. We demonstrate the performance of our SMOFlow method on real stabilized aerial videos fully representative of future satellite conditions. SMOFlow favorably compares to other methods. Our SMOFlow method is able to accurately capture the motion of small objects in large images, while efficiently reducing memory consumption.

关键词： computer vision Solid modeling Correlation Satellites Memory management Estimation Satellite images

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iEdit: Localised Text-guided Image Editing with Weak Supervision

iEdit: Localised Text-guided Image Editing with Weak Supervi...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Rumeysa Bodur Erhan Gundogdu Binod Bhattarai Tae-Kyun Kim Michael Donoser Loris Bazzani Imperial College London UK Amazon University of Aberdeen UK KAIST South Korea

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Diffusion models (DMs) can generate realistic images with text guidance using large-scale datasets. However, they demonstrate limited controllability on the generated images. We introduce iEdit, a novel method for text-guided image editing conditioned on a source image and textual prompt. As a fully-annotated dataset with target images does not exist, previous approaches perform subject-specific fine-tuning at test time or adopt contrastive learning without a target image, leading to issues on preserving source image fidelity. We propose to automatically construct a dataset derived from LAION-5B, containing pseudo-target images and descriptive edit prompts. The dataset allows us to incorporate a weakly-supervised loss function, generating the pseudo-target image from the source image’s latent noise conditioned on the edit prompt. To encourage localised editing we propose a loss function that uses segmentation masks to guide the editing during training and optionally at inference. Trained with limited GPU resources on the constructed dataset, our model outperforms counterparts in image fidelity, CLIP alignment score, and qualitatively for both generated and real images.

关键词： Training Image segmentation computer vision conferences Noise Graphics processing units Contrastive learning

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3D Human Pose Estimation with Occlusions: Introducing BlendMimic3D Dataset and GCN Refinement

3D Human Pose Estimation with Occlusions: Introducing BlendM...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Filipa Lino Carlos Santiago Manuel Marques LARSyS Instituto Superior Técnico Institute for Systems and Robotics Portugal

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In the field of 3D Human Pose Estimation (HPE), accurately estimating human pose, especially in scenarios with occlusions, is a significant challenge. This work identifies and addresses a gap in the current state of the art in 3D HPE concerning the scarcity of data and strategies for handling occlusions. We introduce our novel BlendMimic3D dataset, designed to mimic real-world situations where occlusions occur for seamless integration in 3D HPE algorithms. Additionally, we propose a 3D pose refinement block, employing a Graph Convolutional Network (GCN) to enhance pose representation through a graph model. This GCN block acts as a plug-and-play solution, adaptable to various 3D HPE frameworks without requiring retraining them. By training the GCN with occluded data from Blend-Mimic3D, we demonstrate significant improvements in resolving occluded poses, with comparable results for non-occluded ones. Project web page is available at https://***/BlendMimic3D/.

关键词： Training Solid modeling computer vision Three-dimensional displays Graph convolutional networks conferences Pose estimation

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De-noised vision-language Fusion Guided by Visual Cues for E-commerce Product Search

De-noised Vision-language Fusion Guided by Visual Cues for E...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Zhizhang Hu Shasha Li Ming Du Arnab Dhua Douglas Gray University of California Merced Amazon Visual Search & AR

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

In e-commerce applications, vision-language multimodal transformer models play a pivotal role in product search. The key to successfully training a multimodal model lies in the alignment quality of image-text pairs in the dataset. However, the data in practice is often automatically collected with minimal manual intervention. Hence the alignment of image-text pairs is far from ideal. In e-commerce, this misalignment can stem from noisy and redundant non-visual-descriptive text attributes in the product description. To address this, we introduce the MultiModal alignment-guided Learned Token Pruning (MM-LTP) method. MM-LTP employs token pruning, conventionally used for computational efficiency, to perform online text cleaning during multimodal model training. By enabling the model to discern and discard unimportant tokens, it is able to train with implicitly cleaned image-text pairs. We evaluate MM-LTP using a benchmark multimodal e-commerce dataset comprising over 710,000 unique Amazon products. Our evaluation hinges on visual search, a prevalent e-commerce feature. Through MM-LTP, we demonstrate that refining text tokens enhances the paired image branch's training, which leads to significantly improved visual search performance.

关键词： Training Visualization Computational modeling Refining Manuals Fasteners Transformers

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A Data-Centric Solution to NonHomogeneous Dehazing via vision Transformer

A Data-Centric Solution to NonHomogeneous Dehazing via Visio...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Yangyi Liu Huan Liu Liangyan Li Zijun Wu Jun Chen McMaster University Hamilton Canada China Telecom Research Institute Shanghai China

Recent years have witnessed an increased interest in image dehazing. Many deep learning methods have been proposed to tackle this challenge, and have made significant accomplishments dealing with homogeneous haze. However, these solutions cannot maintain comparable performance when they are applied to images with non-homogeneous haze, e.g., NH-HAZE23 dataset introduced by NTIRE challenge. One of the reasons for such failures is that non-homogeneous haze does not obey one of the assumptions that is required for modeling homogeneous haze. In addition, a large number of pairs of non-homogeneous hazy image and the clean counterpart is required using traditional end-to-end training approaches, while NH-HAZE23 dataset is of limited quantities. Although it is possible to augment the NH-HAZE23 dataset by leveraging other non-homogeneous dehazing datasets, we observe that it is necessary to design a proper data-preprocessing technique that reduces the distribution gaps between the target dataset and the augmented one. This finding indeed aligns with the essence of data-centric AI. With a novel network architecture and a principled data-preprocessing approach that systematically enhances data quality, we present an innovative dehazing method. Specifically, we apply RGB-channel-wise transformations on the augmented datasets, and incorporate the state-of-the-art transformers as the backbone in the two-branch framework. We conduct extensive experiments and ablation studies to demonstrate the effectiveness of our proposed method. The source code is available at https://***/yangyiliu21/ntire2023_ITBdehaze.

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Improving Object Detection to Fisheye Cameras with Open-Vocabulary Pseudo-Label Approach

Improving Object Detection to Fisheye Cameras with Open-Voca...

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ieee computer Society conference on computer vision and pattern recognition workshops (cvprw)

作者： Long Hoang Pham Quoc Pham-Nam Ho Duong Nguyen-Ngoc Tran Tai Huu-Phuong Tran Huy-Hung Nguyen Duong Khac Vu Chi Dai Tran Ngoc Doan-Minh Huynh Hyung-Min Jeon Hyung-Joon Jeon Jae Wook Jeon Department of Electrical and Computer Engineering Sungkyunkwan University

ISBN: (数字)9798350365474

ISBN: (纸本)9798350365481

Fish-eye cameras have long been employed in traffic surveillance systems to allow for wider observation of the roads. Despite their widespread use, limited computer vision research is tailored explicitly to images captured by fish-eye cameras. The AI City Challenge 2024 - Track 4 introduces a novel fish-eye camera dataset for the 2D road object detection task. This paper proposes a framework designed to detect objects in fish-eye camera images. Our approach involves several key steps: first, we generate image data to bridge the representation gap between day and night images. Next, we leverage zero-shot open vocabulary detection to produce pseudo-labels, aiding in training supervised object detection models. Additionally, we optimize the model’s hyper-parameters and inference configuration for better performance. Finally, we apply various post-processing techniques to enhance detection performance. Our solution achieves a final F1 score of 0.6194 in the AI City Challenge 2024 - Track 4, ranking third among competing teams. The source code is available at GitHub Repo.

关键词： Training computer vision Adaptation models Vocabulary Roads Urban areas Object detection

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