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Neural Scene Graphs for Dynamic Scenes

Neural Scene Graphs for Dynamic Scenes

引用

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

作者： Ost, Julian Mannan, Fahim Thuerey, Nils Knodt, Julian Heide, Felix Algolux Montreal PQ Canada Tech Univ Munich Munich Germany Princeton Univ Princeton NJ 08544 USA

ISBN: (纸本)9781665445092

Recent implicit neural rendering methods have demonstrated that it is possible to learn accurate view synthesis for complex scenes by predicting their volumetric density and color supervised solely by a set of RGB images. However, existing methods are restricted to learning efficient representations of static scenes that encode all scene objects into a single neural network, and they lack the ability to represent dynamic scenes and decompose scenes into individual objects. In this work, we present the first neural rendering method that represents multi-object dynamic scenes as scene graphs. We propose a learned scene graph representation, which encodes object transformations and radiance, allowing us to efficiently render novel arrangements and views of the scene. To this end, we learn implicitly encoded scenes, combined with a jointly learned latent representation to describe similar objects with a single implicit function. We assess the proposed method on synthetic and real automotive data, validating that our approach learns dynamic scenes - only by observing a video of this scene - and allows for rendering novel photo-realistic views of novel scene compositions with unseen sets of objects at unseen poses.

关键词： Training computer vision Extrapolation Image color analysis Neural networks Training data Object detection

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Rethinking the Evaluation of Video Summaries 32

Rethinking the Evaluation of Video Summaries

引用

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

作者： Otani, Mayu Nakashima, Yuta Rahtu, Esa Heikkila, Janne CyberAgent Inc Tokyo Japan Osaka Univ Suita Osaka Japan Tampere Univ Tampere Finland Univ Oulu Oulu Finland

ISBN: (纸本)9781728132938

Video summarization is a technique to create a short skim of the original video while preserving the main stories/content. There exists a substantial interest in automatizing this process due to the rapid growth of the available material. The recent progress has been facilitated by public benchmark datasets, which enable easy and fair comparison of methods. Currently the established evaluation protocol is to compare the generated summary with respect to a set of reference summaries provided by the dataset. In this paper,we will provide in-depth assessment of this pipeline using two popular benchmark datasets. Surprisingly, we observe that randomly generated summaries achieve comparable or better performance to the state-of-the-art. In some cases, the random summaries outperform even the human generated summaries in leave-one-out experiments. Moreover it turns out that the video segmentation, which is often considered as a fixed pre-processing method, has the most significant impact on the performance measure. Based on our observations,we propose alternative approaches for assessing the importance scores as well as an intuitive visualization of correlation between the estimated scoring and human annotations.

关键词： Datasets and Evaluation vision Applications and Systems

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Transfer4D: A framework for frugal motion capture and deformation transfer

Transfer4D: A framework for frugal motion capture and deform...

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

作者： Maheshwari, Shubh Narain, Rahul Hebbalaguppe, Ramya TCS Res Gurugram India Indian Inst Technol Delhi New Delhi India

ISBN: (纸本)9798350301298

Animating a virtual character based on a real performance of an actor is a challenging task that currently requires expensive motion capture setups and additional effort by expert animators, rendering it accessible only to large production houses. The goal of our work is to democratize this task by developing a frugal alternative termed "Transfer4D" that uses only commodity depth sensors and further reduces animators' effort by automating the rigging and animation transfer process. Our approach can transfer motion from an incomplete, single-view depth video to a semantically similar target mesh, unlike prior works that make a stricter assumption on the source to be noise-free and watertight. To handle sparse, incomplete videos from depth video inputs and variations between source and target objects, we propose to use skeletons as an intermediary representation between motion capture and transfer. We propose a novel unsupervised skeleton extraction pipeline from a single-view depth sequence that incorporates additional geometric information, resulting in superior performance in motion reconstruction and transfer in comparison to the contemporary methods and making our approach generic. We use non-rigid reconstruction to track motion from the depth sequence, and then we rig the source object using skinning decomposition. Finally, the rig is embedded into the target object for motion retargeting.

关键词： vision + graphics

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Shape Unicode: A Unified Shape Representation 32

Shape Unicode: A Unified Shape Representation

引用

32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Muralikrishnan, Sanjeev Kim, Vladimir G. Fisher, Matthew Chaudhuri, Siddhartha Adobe Res San Jose CA 95110 USA Indian Inst Technol Mumbai Maharashtra India

ISBN: (纸本)9781728132938

3D shapes come in varied representations from a set of points to a set of images, each capturing different aspects of the shape. We propose a unified code for 3D shapes, dubbed Shape Unicode, that imbibes shape cues across these representations into a single code, and a novel framework to learn such a code space for any 3D shape dataset. We discuss this framework as a single go-to training model for any input representation, and demonstrate the effectiveness of the learned code space by applying it directly to common shape analysis tasks - discriminative and generative. In this work, we use three common representations - voxel grids, point clouds and multi-view projections - and combine them into a single code. Note that while we use all three representations at training time, the code can be derived from any single representation during testing. We evaluate this code space on shape retrieval, segmentation and correspondence, and show that the unified code performs better than the individual representations themselves. Additionally, this code space compares quite well to the representation-specific state-of-the-art in these tasks. We also qualitatively discuss linear interpolation between points in this space, by synthesizing from intermediate points.

关键词： Representation Learning vision + Graphics

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Transformer Based Line Segment Classifier with Image Context for Real-Time Vanishing Point Detection in Manhattan World

Transformer Based Line Segment Classifier with Image Context...

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

作者： Tong, Xin Ying, Xianghua Shi, Yongjie Wang, Ruibin Yang, Jinfa Peking Univ Sch Artificial Intelligence Key Lab Machine Percept MoE Beijing Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Previous works on vanishing point detection usually use geometric prior for line segment clustering. We find that image context can also contribute to accurate line classification. Based on this observation, we propose to classify line segments into three groups according to three unknown-but-sought vanishing points with Manhattan world assumption, using both geometric information and image context in this work. To achieve this goal, we propose a novel Transformer based Line segment Classifier (TLC) that can group line segments in images and estimate the corresponding vanishing points. In TLC, we design a line segment descriptor to represent line segments using their positions, directions and local image contexts. Transformer based feature fusion module is used to capture global features from all line segments, which is proved to improve the classification performance significantly in our experiments. By using a network to score line segments for outlier rejection, vanishing points can be got by Singular Value Decomposition (SVD) from the classified lines. The proposed method runs at 25 fps on one NVIDIA 2080Ti card for vanishing point detection. Experimental results on synthetic and real-world datasets demonstrate that our method is superior to other state-of-the-art methods on the balance between accuracy and efficiency, while keeping stronger generalization capability when trained and evaluated on different datasets.

关键词： Image segmentation computer vision computer architecture Predictive models Transformers Real-time systems pattern recognition

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Using Unknown Occluders to Recover Hidden Scenes 32

Using Unknown Occluders to Recover Hidden Scenes

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

作者： Yedidia, Adam B. Baradad, Manel Thrampoulidis, Christos Freeman, William T. Wornell, Gregory W. MIT Cambridge MA 02139 USA UC Santa Barbara Santa Barbara CA USA Google Res Mountain View CA USA

ISBN: (纸本)9781728132938

We consider the challenging problem of inferring a hidden moving scene from faint shadows cast on a diffuse surface. Recent work in passive non-line-of-sight (NLoS) imaging has shown that the presence of occluding objects in between the scene and the diffuse surface significantly improves the conditioning of the problem I I. However, that work assumes that the shape of the occluder is known a priori. In this paper, we relax this often impractical assumption, extending the range of applications for passive occluder-based NLoS imaging systems. We formulate the task of jointly recovering the unknown scene and unknown occluder as a blind deconvolution problem, for which we propose a simple but effective two-step algorithm. At the first step, the algorithm exploits motion in the scene in order to obtain an estimate of the occluder. In particular, it exploits the fact that motion in realistic scenes is typically sparse. The second step is more standard: using regularization, we deconvolve by the occluder estimate to solve for the hidden scene. We demonstrate the effectiveness of our method with simulations and experiments in a variety of settings.

关键词： Computational Photography Scene Analysis and Understanding vision Applications and Systems

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ZeroWaste Dataset: Towards Deformable Object Segmentation in Cluttered Scenes

ZeroWaste Dataset: Towards Deformable Object Segmentation in...

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

作者： Bashkirova, Dina Abdelfattah, Mohamed Zhu, Ziliang Akl, James Alladkani, Fadi Hu, Ping Ablavsky, Vitaly Calli, Berk Bargal, Sarah Adel Saenko, Kate Boston Univ Boston MA 02215 USA Amer Univ Cairo Cairo Egypt Worcester Polytech Inst Worcester MA 01609 USA Univ Washington Washington DC USA MIT IBM Watson AI Lab Cambridge MA USA

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Less than 35% of recyclable waste is being actually recycled in the US [2], which leads to increased soil and sea pollution and is one of the major concerns of environmental researchers as well as the common public. At the heart of the problem are the inefficiencies of the waste sorting process (separating paper, plastic, metal, glass, etc.) due to the extremely complex and cluttered nature of the waste stream. Recyclable waste detection poses a unique computer vision challenge as it requires detection of highly deformable and often translucent objects in cluttered scenes without the kind of context information usually present in human-centric datasets. This challenging computer vision task currently lacks suitable datasets or methods in the available literature. In this paper, we take a step towards computer-aided waste detection and present the first in-the-wild industrial-grade waste detection and segmentation dataset, ZeroWaste. We believe that ZeroWaste will catalyze research in object detection and semantic segmentation in extreme clutter as well as applications in the recycling domain. Our project page can be found at http://***/zerowaste/

关键词： Training computer vision Pollution Transfer learning Semantics Soil Recycling

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Joint Distribution Matters: Deep Brownian Distance Covariance for Few-Shot Classification

Joint Distribution Matters: Deep Brownian Distance Covarianc...

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

作者： Xie, Jiangtao Long, Fei Lv, Jiaming Wang, Qilong Li, Peihua Dalian Univ Technol Dalian Peoples R China Tianjin Univ Tianjin Peoples R China

ISBN: (数字)9781665469463

ISBN: (纸本)9781665469463

Few-shot classification is a challenging problem as only very few training examples are given for each new task. One of the effective research lines to address this challenge focuses on learning deep representations driven by a similarity measure between a query image and few support images of some class. Statistically, this amounts to measure the dependency of image features, viewed as random vectors in a high-dimensional embedding space. Previous methods either only use marginal distributions without considering joint distributions, suffering from limited representation capability, or are computationally expensive though harnessing joint distributions. In this paper, we propose a deep Brownian Distance Covariance (DeepBDC) method for few-shot classification. The central idea of DeepBDC is to learn image representations by measuring the discrepancy between joint characteristic functions of embedded features and product of the marginals. As the BDC metric is decoupled, we formulate it as a highly modular and efficient layer. Furthermore, we instantiate DeepBDC in two different few-shot classification frameworks. We make experiments on six standard few-shot image benchmarks, covering general object recognition, fine-grained categorization and cross-domain classification. Extensive evaluations show our DeepBDC significantly outperforms the counterparts, while establishing new state-of-the-art results. The source code is available at http://***/DeepBDC.

关键词： Training Deep learning computer vision Transfer learning Image representation Extraterrestrial measurements pattern recognition

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Divide and Conquer: Answering Questions with Object Factorization and Compositional Reasoning

Divide and Conquer: Answering Questions with Object Factoriz...

引用

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

作者： Chen, Shi Zhao, Qi Univ Minnesota Dept Comp Sci & Engn Minneapolis MN 55455 USA

ISBN: (纸本)9798350301298

Humans have the innate capability to answer diverse questions, which is rooted in the natural ability to correlate different concepts based on their semantic relationships and decompose difficult problems into sub-tasks. On the contrary, existing visual reasoning methods assume training samples that capture every possible object and reasoning problem, and rely on black-boxed models that commonly exploit statistical priors. They have yet to develop the capability to address novel objects or spurious biases in real-world scenarios, and also fall short of interpreting the rationales behind their decisions. Inspired by humans' reasoning of the visual world, we tackle the aforementioned challenges from a compositional perspective, and propose an integral framework consisting of a principled object factorization method and a novel neural module network. Our factorization method decomposes objects based on their key characteristics, and automatically derives prototypes that represent a wide range of objects. With these prototypes encoding important semantics, the proposed network then correlates objects by measuring their similarity on a common semantic space and makes decisions with a compositional reasoning process. It is capable of answering questions with diverse objects regardless of their availability during training, and overcoming the issues of biased question-answer distributions. In addition to the enhanced generalizability, our framework also provides an interpretable interface for understanding the decision-making process of models. Our code is available at https://***/szzexpoi/POEM.

关键词： and reasoning language vision

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Multi-Agent Tensor Fusion for Contextual Trajectory Prediction 32

Multi-Agent Tensor Fusion for Contextual Trajectory Predicti...

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32nd ieee/CVF conference on computer vision and pattern recognition (CVPR)

作者： Zhao, Tianyang Xu, Yifei Monfort, Mathew Choi, Wongun Baker, Chris Zhao, Yibiao Wang, Yizhou Wu, Ying Nian ISEE AI Cambridge MA USA Peking Univ Beijing Peoples R China Univ Calif Los Angeles Los Angeles CA 90024 USA MIT CSAIL Cambridge MA USA

ISBN: (纸本)9781728132938

Accurate prediction of others' trajectories is essential for autonomous driving. Trajectory prediction is challenging because it requires reasoning about agents' past movements, social interactions among varying numbers and kinds of agents, constraints from the scene context, and the stochasticity of human behavior. Our approach models these interactions and constraints jointly within a novel Multi-Agent Tensor Fusion (MATF) network. Specifically, the model encodes multiple agents' past trajectories and the scene context into a Multi-Agent Tensor, then applies convolutional fusion to capture multiagent interactions while retaining the spatial structure of agents and the scene context. The model decodes recurrently to multiple agents' future trajectories, using adversarial loss to learn stochastic predictions. Experiments on both highway driving and pedestrian crowd datasets show that the model achieves state-of-the-art prediction accuracy.

关键词： Deep Learning Motion and Tracking Robotics + Driving Scene Analysis and Understanding vision Applications and System

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