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arXiv 2022年

作者： Yin, Junbo Fang, Jin Zhou, Dingfu Zhang, Liangjun Xu, Cheng-Zhong Shen, Jianbing Wang, Wenguan School of Computer Science Beijing Institute of Technology China Baidu Research United States National Engineering Laboratory of Deep Learning Technology and Application China SKL-IOTSC Cis University of Macau China ReLER Aaii University of Technology Sydney Australia

Dominated point cloud-based 3D object detectors in autonomous driving scenarios rely heavily on the huge amount of accurately labeled samples, however, 3D annotation in the point cloud is extremely tedious, expensive and time-consuming. To reduce the dependence on large supervision, semi-supervised learning (SSL) based approaches have been proposed. The Pseudo-Labeling methodology is commonly used for SSL frameworks, however, the low-quality predictions from the teacher model have seriously limited its performance. In this work, we propose a new Pseudo-Labeling framework for semi-supervised 3D object detection, by enhancing the teacher model to a proficient one with several necessary designs. First, to improve the recall of pseudo labels, a Spatial-temporal Ensemble (STE) module is proposed to generate sufficient seed boxes. Second, to improve the precision of recalled boxes, a Clustering-based Box Voting (CBV) module is designed to get aggregated votes from the clustered seed boxes. This also eliminates the necessity of sophisticated thresholds to select pseudo labels. Furthermore, to reduce the negative influence of wrongly pseudo-labeled samples during the training, a soft supervision signal is proposed by considering Box-wise Contrastive learning (BCL). The effectiveness of our model is verified on both ONCE and Waymo datasets. For example, on ONCE, our approach significantly improves the baseline by 9.51 mAP. Moreover, with half annotations, our model outperforms the oracle model with full annotations on Waymo. Copyright © 2022, The Authors. All rights reserved.

关键词： Object detection

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LiDAR-Based Online 3D Video Object Detection With Graph-Based Message Passing and Spatiotemporal Transformer Attention

LiDAR-Based Online 3D Video Object Detection With Graph-Base...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Junbo Yin Jianbing Shen Chenye Guan Dingfu Zhou Ruigang Yang Beijing Lab of Intelligent Information Technology School of Computer Science Beijing Institute of Technology China Baidu Research Inception Institute of Artificial Intelligence UAE National Engineering Laboratory of Deep Learning Technology and Application China University of Kentucky Kentucky USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Existing LiDAR-based 3D object detectors usually focus on the single-frame detection, while ignoring the spatiotemporal information in consecutive point cloud frames. In this paper, we propose an end-to-end online 3D video object detector that operates on point cloud sequences. The proposed model comprises a spatial feature encoding component and a spatiotemporal feature aggregation component. In the former component, a novel Pillar Message Passing Network (PMPNet) is proposed to encode each discrete point cloud frame. It adaptively collects information for a pillar node from its neighbors by iterative message passing, which effectively enlarges the receptive field of the pillar feature. In the latter component, we propose an Attentive Spatiotemporal Transformer GRU (AST-GRU) to aggregate the spatiotemporal information, which enhances the conventional ConvGRU with an attentive memory gating mechanism. AST-GRU contains a Spatial Transformer Attention (STA) module and a Temporal Transformer Attention (TTA) module, which can emphasize the foreground objects and align the dynamic objects, respectively. Experimental results demonstrate that the proposed 3D video object detector achieves state-of-the-art performance on the large-scale nuScenes benchmark.

关键词： Three-dimensional displays Feature extraction Spatiotemporal phenomena Detectors Object detection Encoding Message passing

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ApolloCar3D: A Large 3D Car Instance Understanding Benchmark for Autonomous Driving

arXiv

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arXiv 2018年

作者： Song, Xibin Wang, Peng Zhou, Dingfu Zhu, Rui Guan, Chenye Dai, Yuchao Su, Hao Li, Hongdong Yang, Ruigang Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China University of California San Diego United States Northwestern Polytechnical University Xi’an China Australian National University Australia Australian Centre for Robotic Vision Australia

Autonomous driving has attracted remarkable attention from both industry and academia. An important task is to estimate 3D properties (e.g. translation, rotation and shape) of a moving or parked vehicle on the road. This task, while critical, is still under-researched in the computer vision community – partially owing to the lack of large scale and fully-annotated 3D car database suitable for autonomous driving research. In this paper, we contribute the first large-scale database suitable for 3D car instance understanding – ApolloCar3D. The dataset contains 5,277 driving images and over 60K car instances, where each car is fitted with an industry-grade 3D CAD model with absolute model size and semantically labelled keypoints. This dataset is above 20× larger than PASCAL3D+ [65] and KITTI [21], the current state-of-the-art. To enable efficient labelling in 3D, we build a pipeline by considering 2D-3D keypoint correspondences for a single instance and 3D relationship among multiple instances. Equipped with such dataset, we build various baseline algorithms with the state-of-the-art deep convolutional neural networks. Specifically, we first segment each car with a pre-trained Mask R-CNN [22], and then regress towards its 3D pose and shape based on a deformable 3D car model with or without using semantic keypoints. We show that using keypoints significantly improves fitting performance. Finally, we develop a new 3D metric jointly considering 3D pose and 3D shape, allowing for comprehensive evaluation and ablation study. By comparing with human performance we suggest several future directions for further improvements. Copyright © 2018, The Authors. All rights reserved.

关键词： Autonomous vehicles

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LAE : Long-tailed Age Estimation

arXiv

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arXiv 2021年

作者： Bao, Zenghao Tan, Zichang Zhu, Yu Wan, Jun Ma, Xibo Lei, Zhen Guo, Guodong CBSR NLPR Institute of Automation Chinese Academy of Sciences School of Artificial Intelligence University of Chinese Academy of Sciences Centre for Artificial Intelligence and Robotics Hong Kong Institute of Science & Innovation Chinese Academy of Sciences Institute of Deep Learning Baidu Research National Engineering Laboratory for Deep Learning Technology and Application

Facial age estimation is an important yet very challenging problem in computer vision. To improve the performance of facial age estimation, we first formulate a simple standard baseline and build a much strong one by collecting the tricks in pre-training, data augmentation, model architecture, and so on. Compared with the standard baseline, the proposed one significantly decreases the estimation errors. Moreover, long-tailed recognition has been an important topic in facial age datasets, where the samples often lack on the elderly and children. To train a balanced age estimator, we propose a two-stage training method named Long-tailed Age Estimation (LAE), which decouples the learning procedure into representation learning and classification. The effectiveness of our approach has been demonstrated on the dataset provided by organizers of Guess The Age Contest 2021. Copyright © 2021, The Authors. All rights reserved.

关键词： deep learning

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Channel Attention Based Iterative Residual learning for Depth Map Super-Resolution

Channel Attention Based Iterative Residual Learning for Dept...

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Conference on Computer Vision and Pattern Recognition (CVPR)

作者： Xibin Song Yuchao Dai Dingfu Zhou Liu Liu Wei Li Hongdong Li Ruigang Yang Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China Northwestern Polytechnical University China Australian National University Australia Australian Centre for Robotic Vision Australia Shandong University China University of Kentucky Kentucky USA

ISBN: (数字)9781728171685

ISBN: (纸本)9781728171692

Despite the remarkable progresses made in deep learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a real depth sensor. In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with realworld DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. First, we propose to classify the generation of LR depth maps into two types: non-linear downsampling with noise and interval downsampling, for which DSR models are learned correspondingly. Second, we propose a new framework for real-world DSR, which consists of four modules : 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner; 2) A channel attention strategy to enhance channels with abundant high-frequency components; 3) A multi-stage fusion module to effectively reexploit the results in the coarse-to-fine process; and 4) A depth refinement module to improve the depth map by TGV regularization and input loss. Extensive experiments on benchmarking datasets demonstrate the superiority of our method over current state-of-the-art DSR methods.

关键词： Degradation Iron Color Kernel Image resolution Three-dimensional displays Feature extraction

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Detailed human shape estimation from a single image by hierarchical mesh deformation

arXiv

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arXiv 2019年

作者： Zhu, Hao Zuo, Xinxin Wang, Sen Cao, Xun Yang, Ruigang Nanjing University Nanjing China University of Kentucky LexingtonKY United States Northwestern Polytechnical University Xi'an China Baidu Inc. Beijing China National Engineering Laboratory of Deep Learning and Technology and Application China

This paper presents a novel framework to recover detailed human body shapes from a single image. It is a challenging task due to factors such as variations in human shapes, body poses, and viewpoints. Prior methods typically attempt to recover the human body shape using a parametric based template that lacks the surface details. As such the resulting body shape appears to be without clothing. In this paper, we propose a novel learningbased framework that combines the robustness of parametric model with the flexibility of free-form 3D deformation. We use the deep neural networks to refine the 3D shape in a Hierarchical Mesh Deformation (HMD) framework, utilizing the constraints from body joints, silhouettes, and per-pixel shading information. We are able to restore detailed human body shapes beyond skinned models. Experiments demonstrate that our method has outperformed previous state-of-the-art approaches, achieving better accuracy in terms of both 2D IoU number and 3D metric distance. The code is available in https://***/zhuhao-nju/***. Copyright © 2019, The Authors. All rights reserved.

关键词： deep neural networks

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Channel attention based iterative residual learning for depth map super-resolution

arXiv

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arXiv 2020年

作者： Song, Xibin Dai, Yuchao Zhou, Dingfu Liu, Liu Li, Wei Li, Hongdong Yang, Ruigang Baidu Research National Engineering Laboratory of Deep Learning Technology and Application China Northwestern Polytechnical University China Shandong University China Australian National University Australia Australian Centre for Robotic Vision Australia University of Kentucky Kentucky United States

Despite the remarkable progresses made in deep-learning based depth map super-resolution (DSR), how to tackle real-world degradation in low-resolution (LR) depth maps remains a major challenge. Existing DSR model is generally trained and tested on synthetic dataset, which is very different from what would get from a real depth sensor. In this paper, we argue that DSR models trained under this setting are restrictive and not effective in dealing with real-world DSR tasks. We make two contributions in tackling real-world degradation of different depth sensors. First, we propose to classify the generation of LR depth maps into two types: non-linear downsampling with noise and interval downsampling, for which DSR models are learned correspondingly. Second, we propose a new framework for real-world DSR, which consists of four modules: 1) An iterative residual learning module with deep supervision to learn effective high-frequency components of depth maps in a coarse-to-fine manner;2) A channel attention strategy to enhance channels with abundant high-frequency components;3) A multi-stage fusion module to effectively re-exploit the results in the coarse-to-fine process;and 4) A depth refinement module to improve the depth map by TGV regularization and input loss. Extensive experiments on benchmarking datasets demonstrate the superiority of our method over current state-of-the-art DSR methods. Copyright © 2020, The Authors. All rights reserved.

关键词： Optical resolving power

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CASIA-SURF: A Large-scale Multi-modal Benchmark for Face Anti-spoofing

arXiv

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arXiv 2019年

作者： Zhang, Shifeng Liu, Ajian Wan, Jun Liang, Yanyan Guo, Guogong Escalera, Sergio Escalante, Hugo Jair Li, Stan Z. National Laboratory of Pattern Recognition Institute of Automation Chinese Academy of Sciences University of Chinese Academy of Sciences Beijing China Macau University of Science and Technology Macau China Institute of Deep Learning Baidu Research and National Engineering Laboratory for Deep Learning Technology and Application Universitat de Barcelona Computer Vision Center Barcelona Catalonia Instituto Nacional de Astrofsica Ptica y Electrnica Puebla72840 Mexico

Face anti-spoofing is essential to prevent face recognition systems from a security breach. Much of the progresses have been made by the availability of face anti-spoofing benchmark datasets in recent years. However, existing face antispoofing benchmarks have limited number of subjects (≤170) and modalities (≤2), which hinder the further development of the academic community. To facilitate face anti-spoofing research, we introduce a large-scale multi-modal dataset, namely CASIASURF, which is the largest publicly available dataset for face antispoofing in terms of both subjects and modalities. Specifically, it consists of 1;000 subjects with 21;000 videos and each sample has 3 modalities (i.e., RGB, Depth and IR). We also provide comprehensive evaluation metrics, diverse evaluation protocols, training/validation/testing subsets and a measurement tool, developing a new benchmark for face anti-spoofing. Moreover, we present a novel multi-modal multi-scale fusion method as a strong baseline, which performs feature re-weighting to select the more informative channel features while suppressing the less useful ones for each modality across different scales. Extensive experiments have been conducted on the proposed dataset to verify its significance and generalization capability. The dataset is available at http://***/***/chalearnfacespoofingattackdete/. Copyright © 2019, The Authors. All rights reserved.

关键词： Face recognition

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FaceScape: A large-scale high quality 3D face dataset and detailed riggable 3D face prediction

arXiv

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arXiv 2020年

作者： Yang, Haotian Zhu, Hao Wang, Yanru Huang, Mingkai Shen, Qiu Yang, Ruigang Cao, Xun Nanjing University China Baidu Research University of Kentucky United States Inceptio Inc National Engineering Laboratory for Deep Learning Technology and Applications China

In this paper, we present a large-scale detailed 3D face dataset, FaceScape, and propose a novel algorithm that is able to predict elaborate riggable 3D face models from a single image input. FaceScape dataset provides 18,760 textured 3D faces, captured from 938 subjects and each with 20 specific expressions. The 3D models contain the pore-level facial geometry that is also processed to be topologically uniformed. These fine 3D facial models can be represented as a 3D morphable model for rough shapes and displacement maps for detailed geometry. Taking advantage of the large-scale and high-accuracy dataset, a novel algorithm is further proposed to learn the expression-specific dynamic details using a deep neural network. The learned relationship serves as the foundation of our 3D face prediction system from a single image input. Different than the previous methods, our predicted 3D models are riggable with highly detailed geometry under different expressions. The unprecedented dataset and code will be released to public for research purpose†. Copyright © 2020, The Authors. All rights reserved.

关键词： Geometry

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Invisible for both Camera and LiDAR: Security of multi-sensor fusion based perception in autonomous driving under physical-world attacks

arXiv

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arXiv 2021年

作者： Cao, Yulong Wang, Ningfei Xiao, Chaowei Yang, Dawei Fang, Jin Yang, Ruigang Chen, Qi Alfred Liu, Mingyan Li, Bo University of California Irvine United States University of Michigan United States NVIDIA Research Arizona State University Inceptio Baidu Research and National Engineering Laboratory of Deep Learning Technology and Application China University of Illinois at Urbana-Champaign

In Autonomous Driving (AD) systems, perception is both security and safety critical. Despite various prior studies on its security issues, all of them only consider attacks on camera- or LiDAR-based AD perception alone. However, production AD systems today predominantly adopt a Multi-Sensor Fusion (MSF) based design, which in principle can be more robust against these attacks under the assumption that not all fusion sources are (or can be) attacked at the same time. In this paper, we present the first study of security issues of MSF-based perception in AD systems. We directly challenge the basic MSF design assumption above by exploring the possibility of attacking all fusion sources simultaneously. This allows us for the first time to understand how much security guarantee MSF can fundamentally provide as a general defense strategy for AD perception. We formulate the attack as an optimization problem to generate a physically-realizable, adversarial 3D-printed object that misleads an AD system to fail in detecting it and thus crash into it. To systematically generate such a physical-world attack, we propose a novel attack pipeline that addresses two main design challenges: (1) non-differentiable target camera and LiDAR sensing systems, and (2) non-differentiable cell-level aggregated features popularly used in LiDAR-based AD perception. We evaluate our attack on MSF algorithms included in representative open-source industry-grade AD systems in real-world driving scenarios. Our results show that the attack achieves over 90% success rate across different object types and MSF algorithms. Our attack is also found stealthy, robust to victim positions, transferable across MSF algorithms, and physical-world realizable after being 3D-printed and captured by LiDAR and camera devices. To concretely assess the end-to-end safety impact, we further perform simulation evaluation and show that it can cause a 100% vehicle collision rate for an industry-grade AD system. We also evalu

关键词： Autonomous vehicles

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