Comprehensive understanding of dynamic scenes is a critical prerequisite for intelligent robots to autonomously operate in their environment. Research in this domain, which encompasses diverse perception problems, has primarily been focused on addressing specific tasks individually rather than modeling the ability to understand dynamic scenes holistically. In this paper, we introduce a novel perception task denoted as multi-object panoptic tracking (MOPT), which unifies the conventionally disjoint tasks of semantic segmentation, instance segmentation, and multi-object tracking. MOPT allows for exploiting pixel-level semantic information of 'thing' and 'stuff' classes, temporal coherence, and pixel-level associations over time, for the mutual benefit of each of the individual sub-problems. To facilitate quantitative evaluations of MOPT in a unified manner, we propose the soft panoptic tracking quality (sPTQ) metric. As a first step towards addressing this task, we propose the novel PanopticTrackNet architecture that builds upon the state-of-the-art top-down panoptic segmentation network EfficientPS by adding a new tracking head to simultaneously learn all sub-tasks in an end-to-end manner. Additionally, we present several strong baselines that combine predictions from state-of-the-art panoptic segmentation and multi-object tracking models for comparison. We present extensive quantitative and qualitative evaluations of both vision-based and LiDAR-based MOPT that demonstrate encouraging results.
In this technical report, we describe our EfficientLPT architecture that won the panoptic tracking challenge in the 7th AI Driving Olympics at NeurIPS 2021. Our architecture builds upon the top-down EfficientLPS panoptic segmentation approach. EfficientLPT consists of a shared backbone with a modified EfficientNet-B5 model comprising the proximity convolution module as the encoder followed by the range-aware FPN to aggregate semantically rich range-aware multi-scale features. Subsequently, we employ two task-specific heads, the scale-invariant semantic head and hybrid task cascade with feedbac…
Motional（Motional 公司）；University of Freiburg（弗赖堡大学）
Panoptic scene understanding and tracking of dynamic agents are essential for robots and automated vehicles to navigate in urban environments. As LiDARs provide accurate illumination-independent geometric depictions of the scene, performing these tasks using LiDAR point clouds provides reliable predictions. However, existing datasets lack diversity in the type of urban scenes and have a limited number of dynamic object instances which hinders both learning of these tasks as well as credible benchmarking of the developed methods. In this paper, we introduce the large-scale Panoptic nuScenes ben…
In this paper, we present an algorithm to tackle a video panoptic segmentation problem, a newly emerging area of research. The video panoptic segmentation is a task that unifies the typical task of panoptic segmentation and multi-object tracking. In other words, it requires generating the instance tracking IDs along with panoptic segmentation results across video sequences. Our proposed video panoptic segmentation algorithm uses the transformer and it can be trained in end-to-end with an input of multiple video frames. We test our method on the STEP dataset and report its performance with rece…
In this paper, the task of video panoptic segmentation is studied and two different methods to solve the task will be proposed. Video panoptic segmentation (VPS) is a recently introduced computer vision task that requires classifying and tracking every pixel in a given video. The nature of this task makes the cost of annotating datasets for it prohibiting. To understand video panoptic segmentation, first, earlier introduced constituent tasks that focus on semantics and tracking separately will be researched. Thereafter, two data-driven approaches which do not require training on a tailored VPS…
Peking University（北京大学）；SenseTime Research（商汤科技研究院）；The University of Sydney（悉尼大学）；Wuhan University（武汉大学）；Hubei Luojia Laboratory（湖北珞珈实验室）；JD Explore Academy（京东探索研究院）
The Depth-aware Video Panoptic Segmentation (DVPS) is a new challenging vision problem that aims to predict panoptic segmentation and depth in a video simultaneously. The previous work solves this task by extending the existing panoptic segmentation method with an extra dense depth prediction and instance tracking head. However, the relationship between the depth and panoptic segmentation is not well explored -- simply combining existing methods leads to competition and needs carefully weight balancing. In this paper, we present PolyphonicFormer, a vision transformer to unify these sub-tasks u…
Panoptic segmentation has become a new standard of visual recognition task by unifying previous semantic segmentation and instance segmentation tasks in concert. In this paper, we propose and explore a new video extension of this task, called video panoptic segmentation. The task requires generating consistent panoptic segmentation as well as an association of instance ids across video frames. To invigorate research on this new task, we present two types of video panoptic datasets. The first is a re-organization of the synthetic VIPER dataset into the video panoptic format to exploit its large…
University of Colorado（科罗拉多大学）；NASA Ames Research Center（美国国家航空航天局艾姆斯研究中心）
A new, machine learning-based approach for automatically generating 3D digital geometries of woven composite textiles is proposed to overcome the limitations of existing analytical descriptions and segmentation methods. In this approach, panoptic segmentation is leveraged to produce instance segmented semantic masks from X-ray computed tomography (CT) images. This effort represents the first deep learning based automated process for segmenting unique yarn instances in a woven composite textile. Furthermore, it improves on existing methods by providing instance-level segmentation on low contras…
1st Place Winner of the 2024 Pixel-level Video Understanding in the Wild (CVPR'24 PVUW) Challenge in Video Panoptic Segmentation and Best Long Video Consistency of Video Semantic Segmentation
Device Solutions Research America, Samsung Semiconductor, Inc.（三星半导体公司美国设备解决方案研究中心）
Technical University Munich（慕尼黑工业大学）；Google Research（谷歌研究院）；RWTH Aachen University（亚琛工业大学）；MPI-IS（马克斯·普朗克智能系统研究所）；University of Tübingen（蒂宾根大学）
The task of assigning semantic classes and track identities to every pixel in a video is called video panoptic segmentation. Our work is the first that targets this task in a real-world setting requiring dense interpretation in both spatial and temporal domains. As the ground-truth for this task is difficult and expensive to obtain, existing datasets are either constructed synthetically or only sparsely annotated within short video clips. To overcome this, we introduce a new benchmark encompassing two datasets, KITTI-STEP, and MOTChallenge-STEP. The datasets contain long video sequences, provi…
Microsoft Research Asia（微软亚洲研究院）；Huazhong University of Science and Technology（华中科技大学）
We present VoxelTrack for multi-person 3D pose estimation and tracking from a few cameras which are separated by wide baselines. It employs a multi-branch network to jointly estimate 3D poses and re-identification (Re-ID) features for all people in the environment. In contrast to previous efforts which require to establish cross-view correspondence based on noisy 2D pose estimates, it directly estimates and tracks 3D poses from a 3D voxel-based representation constructed from multi-view images. We first discretize the 3D space by regular voxels and compute a feature vector for each voxel by av…
OccTrack360: 4D Panoptic Occupancy Tracking from Surround-View Fisheye Cameras
School of Artificial Intelligence and Robotics, Hunan University（人工智能与机器人学院，湖南大学）；State Key Laboratory of Extreme Photonics and Instrumentation, Zhejiang University（极端光子学与仪器国家重点实验室，浙江大学）；National Engineering Research Center of Robot Visual Perception and Control Technology, Hunan University（机器人视觉感知与控制技术国家工程研究中心，湖南大学）
SPORTS: Simultaneous Panoptic Odometry, Rendering, Tracking and Segmentation for Urban Scenes Understanding
School of Information Science and Engineering, Yunnan University（云南大学信息科学与工程学院）；Yunnan University（云南大学）；School of Information and Electronics, Beijing Institute of Technology（北京理工大学信息与电子学院）；Yunnan Key Laboratory of Intelligent Systems and Computing（智能系统与计算重点实验室）
Technical University of Munich（慕尼黑工业大学）；University of Bonn（波恩大学）
Temporal semantic scene understanding is critical for self-driving cars or robots operating in dynamic environments. In this paper, we propose 4D panoptic LiDAR segmentation to assign a semantic class and a temporally-consistent instance ID to a sequence of 3D points. To this end, we present an approach and a point-centric evaluation metric. Our approach determines a semantic class for every point while modeling object instances as probability distributions in the 4D spatio-temporal domain. We process multiple point clouds in parallel and resolve point-to-instance associations, effectively all…
PanoSLAM: Panoptic 3D Scene Reconstruction via Gaussian SLAM
The University of Sydney（悉尼大学）；The University of Hong Kong（香港大学）；ShanghaiTech University（上海科技大学）；The University of Melbourne（墨尔本大学）；Texas A&M University（德克萨斯农工大学）
Smart City applications such as intelligent traffic routing or accident prevention rely on computer vision methods for exact vehicle localization and tracking. Due to the scarcity of accurately labeled data, detecting and tracking vehicles in 3D from multiple cameras proves challenging to explore. We present a massive synthetic dataset for multiple vehicle tracking and segmentation in multiple overlapping and non-overlapping camera views. Unlike existing datasets, which only provide tracking ground truth for 2D bounding boxes, our dataset additionally contains perfect labels for 3D bounding bo…
A Comprehensive Survey on Video Scene Parsing:Advances, Challenges, and Prospects
College of Software, Nankai University Tianjin China；School of Electrical；Electronic Engineering, NTU \& Institute for Infocomm Research, A STAR Singapore；School of Information；College of Computer Science, Nankai University Tianjin China；VCIP, College of Computer Science, Nankai University Tianjin China；Academy for Advanced Interdisciplinary Studies, Nankai University Tianjin China；Nankai International Advanced Research Institute Shenzhen Futian China；College of Software, Nankai University；Electronic Engineering, NTU \& Institute for Infocomm Research, A STAR；College of Computer Science, Nankai University；VCIP, College of Computer Science, Nankai University；Academy for Advanced Interdisciplinary Studies, Nankai University；Nankai International Advanced Research Institute
UNO: Unifying One-stage Video Scene Graph Generation via Object-Centric Visual Representation Learning
FPT Software AI Center（FPT软件AI中心）；Aalborg University（奥尔堡大学）；Pioneer Centre for AI（先锋人工智能中心）；S-Lab, Nanyang Technological University（南洋理工大学S实验室）；AICV Lab, University of Arkansas（阿肯色大学AICV实验室）
