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Neural Parts: Learning Expressive 3D Shape Abstractions with Invertible Neural Networks

Neural Parts: Learning Expressive 3D Shape Abstractions with...

引用

ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Paschalidou, Despoina Katharopoulos, Angelos Geiger, Andreas Fidler, Sanja Max Planck Inst Intelligent Syst Tubingen Tubingen Germany Univ Tubingen Tubingen Germany Idiap Res Inst Martigny Switzerland Ecole Polytech Fed Lausanne EPFL Lausanne Switzerland Max Planck ETH Ctr Learning Syst Zurich Switzerland NVIDIA Santa Clara CA 95051 USA Univ Toronto Toronto ON Canada Vector Inst Toronto ON Canada

ISBN: (纸本)9781665445092

Impressive progress in 3D shape extraction led to representations that can capture object geometries with high fidelity. In parallel, primitive-based methods seek to represent objects as semantically consistent part arrangements. However, due to the simplicity of existing primitive representations, these methods fail to accurately reconstruct 3D shapes using a small number of primitives/parts. We address the trade-off between reconstruction quality and number of parts with Neural Parts, a novel 3D primitive representation that defines primitives using an Invertible Neural Network (INN) which implements homeomorphic mappings between a sphere and the target object. The INN allows us to compute the inverse mapping of the homeomorphism, which in turn, enables the efficient computation of both the implicit surface function of a primitive and its mesh, without any additional post-processing. Our model learns to parse 3D objects into semantically consistent part arrangements without any part-level supervision. Evaluations on ShapeNet, D-FAUST and FreiHAND demonstrate that our primitives can capture complex geometries and thus simultaneously achieve geometrically accurate as well as interpretable reconstructions using an order of magnitude fewer primitives than state-of-the-art shape abstraction methods.

关键词： Geometry Solid modeling computer vision Three-dimensional displays Shape Computational modeling Neural networks

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Delving into Localization Errors for Monocular 3D Object Detection

Delving into Localization Errors for Monocular 3D Object Det...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Ma, Xinzhu Zhang, Yinmin Xu, Dan Zhou, Dongzhan Yi, Shuai Li, Haojie Ouyang, Wanli Univ Sydney Sydney NSW Australia Hong Kong Univ Sci & Technol Hong Kong Peoples R China SenseTime Res Hong Kong Peoples R China Dalian Univ Technol Dalian Peoples R China

ISBN: (纸本)9781665445092

Estimating 3D bounding boxes from monocular images is an essential component in autonomous driving, while accurate 3D object detection from this kind of data is very challenging. In this work, by intensive diagnosis experiments, we quantify the impact introduced by each sub-task and found the 'localization error' is the vital factor in restricting monocular 3D detection. Besides, we also investigate the underlying reasons behind localization errors, analyze the issues they might bring, and propose three strategies. First, we revisit the misalignment between the center of the 2D bounding box and the projected center of the 3D object, which is a vital factor leading to low localization accuracy. Second, we observe that accurately localizing distant objects with existing technologies is almost impossible, while those samples will mislead the learned network. To this end, we propose to remove such samples from the training set for improving the overall performance of the detector. Lastly, we also propose a novel 3D IoU oriented loss for the size estimation of the object, which is not affected by 'localization error'. We conduct extensive experiments on the KITTI dataset, where the proposed method achieves real-time detection and outperforms previous methods by a large margin. The code will be made available at: https://***/xinzhuma/monodle.

关键词： Location awareness Training computer vision Three-dimensional displays Estimation Object detection Detectors

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Sketch, Ground, and Refine: Top-Down Dense Video Captioning

Sketch, Ground, and Refine: Top-Down Dense Video Captioning

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Deng, Chaorui Chen, Shizhe Chen, Da He, Yuan Wu, Qi Univ Adelaide Adelaide SA Australia INRIA Rocquencourt France Alibaba Grp Hangzhou Zhejiang Peoples R China

ISBN: (纸本)9781665445092

The dense video captioning task aims to detect and describe a sequence of events in a video for detailed and coherent storytelling. Previous works mainly adopt a "detect-then-describe" framework, which firstly detects event proposals in the video and then generates descriptions for the detected events. However, the definitions of events are diverse which could be as simple as a single action or as complex as a set of events, depending on different semantic contexts. Therefore, directly detecting events based on video information is ill-defined and hurts the coherency and accuracy of generated dense captions. In this work, we reverse the predominant "detect-then-describe" fashion, proposing a top-down way to first generate paragraphs from a global view and then ground each event description to a video segment for detailed refinement. It is formulated as a Sketch, Ground, and Refine process (SGR). The sketch stage first generates a coarse-grained multi-sentence paragraph to describe the whole video, where each sentence is treated as an event and gets localised in the grounding stage. In the refining stage, we improve captioning quality via refinement-enhanced training and dual-path cross attention on both coarse-grained event captions and aligned event segments. The updated event caption can further adjust its segment boundaries. Our SGR model outperforms state-of-the-art methods on ActivityNet Captioning benchmark under traditional and story-oriented dense caption evaluations.

关键词： Training computer vision Codes Grounding Computational modeling Semantics Benchmark testing

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Rethinking Semantic Segmentation from a Sequence-to-Sequence Perspective with Transformers

Rethinking Semantic Segmentation from a Sequence-to-Sequence...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Zheng, Sixiao Lu, Jiachen Zhao, Hengshuang Zhu, Xiatian Luo, Zekun Wang, Yabiao Fu, Yanwei Feng, Jianfeng Xiang, Tao Torr, Philip H. S. Zhang, Li Fudan Univ Shanghai Peoples R China Univ Oxford Oxford England Univ Surrey Guildford Surrey England Tencent Youtu Lab Shanghai Peoples R China Facebook AI Menlo Pk CA USA

ISBN: (纸本)9781665445092

Most recent semantic segmentation methods adopt a fully-convolutional network (FCN) with an encoder-decoder architecture. The encoder progressively reduces the spatial resolution and learns more abstract/semantic visual concepts with larger receptive fields. Since context modeling is critical for segmentation, the latest efforts have been focused on increasing the receptive field, through either dilated/atrous convolutions or inserting attention modules. However, the encoder-decoder based FCN architecture remains unchanged. In this paper, we aim to provide an alternative perspective by treating semantic segmentation as a sequence-to-sequence prediction task. Specifically, we deploy a pure transformer (i.e., without convolution and resolution reduction) to encode an image as a sequence of patches. With the global context modeled in every layer of the transformer, this encoder can be combined with a simple decoder to provide a powerful segmentation model, termed SEgmentation TRansformer (SETR). Extensive experiments show that SETR achieves new state of the art on ADE20K (50.28% mIoU), Pascal Context (55.83% mIoU) and competitive results on Cityscapes. Particularly, we achieve the first position in the highly competitive ADE2OK test server leaderboard on the day of submission.

关键词： Image segmentation Visualization Semantics Transformers Decoding pattern recognition Servers

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3DInAction: Understanding Human Actions in 3D Point Clouds

3DInAction: Understanding Human Actions in 3D Point Clouds

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conference on computer vision and pattern recognition (cvpr)

作者： Yizhak Ben-Shabat Oren Shrout Stephen Gould Australian National University Technion Israel Institute of Technology

ISBN: (数字)9798350353006

ISBN: (纸本)9798350353013

We propose a novel method for 3D point cloud action recognition. Understanding human actions in RGB videos has been widely studied in recent years, however, its 3D point cloud counterpart remains under-explored despite the clear value that 3D information may bring. This is mostly due to the inherent limitation of the point cloud data modality-lack of structure, permutation invariance, and varying number of points-which makes it difficult to learn a spatiotemporal representation. To address this limitation, we propose the 3DinAction pipeline that first estimates patches moving in time (t-patches) as a key building block, alongside a hierarchical architecture that learns an informative spatiotemporal representation. We show that our method achieves improved performance on existing datasets, including DFAUST and IKEA ASM. Code is publicly available at https://***/sitzikbs/3dincaction.

关键词： Point cloud compression computer vision Three-dimensional displays Codes Architecture Pipelines computer architecture

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Towards Semantic Segmentation of Urban-Scale 3D Point Clouds: A Dataset, Benchmarks and Challenges

Towards Semantic Segmentation of Urban-Scale 3D Point Clouds...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Hu, Qingyong Yang, Bo Khalid, Sheikh Xiao, Wen Trigoni, Niki Markham, Andrew Univ Oxford Oxford England Hong Kong Polytech Univ Hong Kong Peoples R China Sensat Ltd London England Newcastle Univ Newcastle Upon Tyne Tyne & Wear England

ISBN: (纸本)9781665445092

An essential prerequisite for unleashing the potential of supervised deep learning algorithms in the area of 3D scene understanding is the availability of large-scale and richly annotated datasets. However, publicly available datasets are either in relative small spatial scales or have limited semantic annotations due to the expensive cost of data acquisition and data annotation, which severely limits the development of fine-grained semantic understanding in the context of 3D point clouds. In this paper, we present an urban-scale photogrammetric point cloud dataset with nearly three billion richly annotated points, which is three times the number of labeled points than the existing largest photogrammetric point cloud dataset. Our dataset consists of large areas from three UK cities, covering about 7.6 km(2) of the city landscape. In the dataset, each 3D point is labeled as one of 13 semantic classes. We extensively evaluate the performance of state-of-the-art algorithms on our dataset and provide a comprehensive analysis of the results. In particular;we identify several key challenges towards urban-scale point cloud understanding. The dataset is available at https://***/QingyongHu/SensatUrban.

关键词： Deep learning computer vision Three-dimensional displays Costs Annotations Semantics Urban areas

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DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-scale Consistency

DSC-PoseNet: Learning 6DoF Object Pose Estimation via Dual-s...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yang, Zongxin Yu, Xin Yang, Yi Baidu Res Beijing Peoples R China Univ Technol Sydney ReLER Sydney NSW Australia

ISBN: (纸本)9781665445092

Compared to 2D object bounding-box labeling, it is very difficult for humans to annotate 3D object poses, especially when depth images of scenes are unavailable. This paper investigates whether we can estimate the object poses effectively when only RGB images and 2D object annotations are given. To this end, we present a two-step pose estimation framework to attain 6DoF object poses from 2D object bounding-boxes. In the first step, the framework learns to segment objects from real and synthetic data in a weakly-supervised fashion, and the segmentation masks will act as a prior for pose estimation. In the second step, we design a dual-scale pose estimation network, namely DSC-PoseNet, to predict object poses by employing a differential renderer. To be specific, our DSC-PoseNet firstly predicts object poses in the original image scale by comparing the segmentation masks and the rendered visible object masks. Then, we resize object regions to a fixed scale to estimate poses once again. In this fashion, we eliminate large scale variations and focus on rotation estimation, thus facilitating pose estimation. Moreover, we exploit the initial pose estimation to generate pseudo ground-truth to train our DSC-PoseNet in a self-supervised manner. The estimation results in these two scales are ensembled as our final pose estimation. Extensive experiments on widely-used benchmarks demonstrate that our method outperforms state-of-the-art models trained on synthetic data by a large margin and even is on par with several fully-supervised methods.

关键词： Training Image segmentation computer vision Three-dimensional displays Annotations Pose estimation Benchmark testing

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Color Me Good: Branding in the Coloring Style of Movie Posters

Color Me Good: Branding in the Coloring Style of Movie Poste...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Agrawal, Rishabh Sivaprasad, Sarath Pedanekar, Niranjan TCS Res Tata Consultancy Serv Pune 411013 Maharashtra India

ISBN: (纸本)9781665448994

Brand logos are often rendered in a different style based on a context such as an event promotion. For example, Warner Bros. uses a different variety of their brand logo for different movies for promotion and aesthetic appeal. In this paper, we propose an automated method to render brand logos in the coloring style of branding material such as movie posters. For this, we adopt a photo-realistic neural style transfer method using movie posters as the style source. We propose a color-based image segmentation and matching method to assign style segments to logo segments. Using these, we render the well-known Warner Bros. logo in the coloring style of 141 movie posters. We also present survey results where 287 participants rate the machine-stylized logos for their representativeness and visual appeal.

关键词： Image segmentation Visualization computer vision Image color analysis conferences Semantics Brightness

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Combinatorial Learning of Graph Edit Distance via Dynamic Embedding

Combinatorial Learning of Graph Edit Distance via Dynamic Em...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Wang, Runzhong Zhang, Tianqi Yu, Tianshu Yan, Junchi Yang, Xiaokang Shanghai Jiao Tong Univ Dept Comp Sci & Engn Shanghai Peoples R China Shanghai Jiao Tong Univ MoE Key Lab Artificial Intelligence Shanghai Peoples R China Arizona State Univ Tempe AZ 85287 USA

ISBN: (纸本)9781665445092

Graph Edit Distance (GED) is a popular similarity measurement for pairwise graphs and it also refers to the recovery of the edit path from the source graph to the target graph. Traditional A* algorithm suffers scalability issues due to its exhaustive nature, whose search heuristics heavily rely on human prior knowledge. This paper presents a hybrid approach by combing the interpretability of traditional search-based techniques for producing the edit path, as well as the efficiency and adaptivity of deep embedding models to achieve a cost-effective GED solver. Inspired by dynamic programming, node-level embedding is designated in a dynamic reuse fashion and suboptimal branches are encouraged to be pruned. To this end, our method can be readily integrated into A* procedure in a dynamic fashion, as well as significantly reduce the computational burden with a learned heuristic. Experimental results on different graph datasets show that our approach can remarkably ease the search process of A* without sacrificing much accuracy. To our best knowledge, this work is also the first deep learning-based GED method for recovering the edit path.

关键词： computer vision Adaptation models Costs Scalability Heuristic algorithms Computational modeling Search problems

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Perception Matters: Detecting Perception Failures of VQA Models Using Metamorphic Testing

Perception Matters: Detecting Perception Failures of VQA Mod...

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ieee/CVF conference on computer vision and pattern recognition (cvpr)

作者： Yuan, Yuanyuan Wang, Shuai Jiang, Mingyue Chen, Tsong Yueh HKUST Hong Kong Peoples R China Zhejiang Sci Tech Univ Hangzhou Peoples R China Swinburne Univ Technol Hawthorn Vic Australia

ISBN: (纸本)9781665445092

Visual question answering (VQA) takes an image and a natural-language question as input and returns a natural-language answer. To date, VQA models are primarily assessed by their accuracy on high-level reasoning questions. Nevertheless, Given that perception tasks (e.g., recognizing objects) are the building blocks in the compositional process required by high-level reasoning, there is a demanding need to gain insights into how much of a problem lowlevel perception is. Inspired by the principles of software metamorphic testing, we introduce MetaVQA, a model-agnostic framework for benchmarking perception capability of VQA models. Given an image i, MetaVQA is able to synthesize a low-level perception question q. It then jointly transforms (i, q) to one or a set of sub-questions and subimages. MetaVQA checks whether the answer to (i, q) satisfies metamorphic relationships (MRs), denoting perception consistency, with the composed answers of transformed questions and images. Violating MRs denotes a failure of answering perception questions. MetaVQA successfully detects over 4.9 million perception failures made by popular VQA models with metamorphic testing. The state-of-the-art VQA models (e.g., the champion of VQA 2020 Challenge) suffer from perception consistency problems. In contrast, the Oscar VQA models, by using anchor points to align questions and images, show generally better consistency in perception tasks. We hope MetaVQA will revitalize interest in enhancing the low-level perceptual abilities of VQA models, a cornerstone of high-level reasoning.

关键词： Visualization computer vision Computational modeling Transforms Benchmark testing Knowledge discovery Cognition

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