This paper considers a network referred to as SoftGroup for accurate and scalable 3D instance segmentation. Existing state-of-the-art methods produce hard semantic predictions followed by grouping instance segmentation results. Unfortunately, errors stemming from hard decisions propagate into the grouping, resulting in poor overlap between predicted instances and ground truth and substantial false positives. To address the abovementioned problems, SoftGroup allows each point to be associated with multiple classes to mitigate the uncertainty stemming from semantic prediction. It also suppresses false positive instances by learning to categorize them as background. Regarding scalability, the existing fast methods require computational time on the order of tens of seconds on large-scale scenes, which is unsatisfactory and far from applicable for real-time. Our finding is that the $k$-Nearest Neighbor ($k$-NN) module, which serves as the prerequisite of grouping, introduces a computational bottleneck. SoftGroup is extended to resolve this computational bottleneck, referred to as SoftGroup++. The proposed SoftGroup++ reduces time complexity with octree $k$-NN and reduces search space with class-aware pyramid scaling and late devoxelization. Experimental results on various indoor and outdoor datasets demonstrate the efficacy and generality of the proposed SoftGroup and SoftGroup++. Their performances surpass the best-performing baseline by a large margin (6\% $\sim$ 16\%) in terms of AP$_{50}$. On datasets with large-scale scenes, SoftGroup++ achieves a 6$\times$ speed boost on average compared to SoftGroup. Furthermore, SoftGroup can be extended to perform object detection and panoptic segmentation with nontrivial improvements over existing methods. The source code and trained models are available at \url{https://github.com/thangvubk/SoftGroup}.
LSP-DETR: Efficient and Scalable Nuclei Segmentation in Whole-Slide Images
Masaryk University, Faculty of Informatics（马萨里克大学信息学院）；Masaryk University, Institute for Computer Science（马萨里克大学计算机科学研究所）；Masaryk Memorial Cancer Institute（马萨里克纪念癌症研究所）
ParticleSeg3D: A Scalable Out-of-the-Box Deep Learning Segmentation Solution for Individual Particle Characterization from Micro CT Images in Mineral Processing and Recycling
Helmholtz Imaging（亥姆霍兹成像中心）；German Cancer Research Center（德国癌症研究中心）；Helmholtz-Zentrum Dresden-Rossendorf（亥姆霍兹德累斯顿罗森多夫研究中心）；Helmholtz Institute Freiberg for Resource Technology（亥姆霍兹弗赖贝格资源技术研究所）；Heidelberg University Hospital（海德堡大学医院）
RGB-based Semantic Segmentation Using Self-Supervised Depth Pre-Training
King Abdullah University of Science and Technology (KAUST)（沙特国王阿卜杜拉科技大学（KAUST））；Thuwal, Saudi Arabia（沙特阿拉伯图瓦勒（注意：这是地址，不应作为机构提取，原问题中可能因HTML格式导致误判，实际正确机构仅KAUST））
Model Agnostic Preference Optimization for Medical Image Segmentation
DGIST, School of Undergrad Studies（DGIST研究生院）；DGIST, Department of Robotics and Mechatronics Engineering（DGIST机器人与机电工程系）；DGIST, Division of Intelligent Robot（DGIST智能机器人系）
Open-Ended CT Volume Segmentation with Weak Supervision from Language
Department of Electrical Engineering and Computer Science, UC Berkeley（加州大学伯克利分校电气工程与计算机科学系）；Voio；Department of Radiology and Biomedical Imaging, UC San Francisco（加州大学旧金山分校放射学与生物医学成像系）；Computational Precision Health, UC Berkeley and UC San Francisco（加州大学伯克利分校和加州大学旧金山分校计算精准健康中心）
Mask DINO: Towards A Unified Transformer-based Framework for Object Detection and Segmentation
The Hong Kong University of Science and Technology（香港科技大学）；Tsinghua University（清华大学）；International Digital Economy Academy (IDEA)（国际数字经济研究院）；The Hong Kong University of Science and Technology (Guangzhou)（香港科技大学（广州））
Towards scalable organ level 3D plant segmentation: Bridging the data algorithm computing gap
Department of Biological and Environmental Engineering（生物与环境工程系）；Horticulture Section, School of Integrative Plant Science（植物科学整合学院园艺系）；School of Electrical and Computer Engineering（电气与计算机工程学院）
TASAM: Terrain-and-Aware Segment Anything Model for Temporal-Scale Remote Sensing Segmentation
University of Alabama at Birmingham（阿拉巴马大学伯明翰分校）；Wuhan University（武汉大学）；Dalian University of Technology（大连理工大学）；University of Chinese Academy of Sciences（中国科学院大学）
GEAR-Seg: A Grounded Explainable Agent for Reasoning Segmentation and Data Engine
College of Biosystems Engineering and Food Science, Zhejiang University（浙江大学生物系统工程与食品科学学院）；ZJU-Hangzhou Global Scientific and Technological Innovation Center, Zhejiang University（浙江大学杭州国际科创中心）
What Makes Synthetic Data Effective in Image Segmentation
State Key Laboratory of Complex and Critical Software Environment（复杂与关键软件环境国家重点实验室）；Beihang University（北京航空航天大学）；School of Computer Science and Engineering（计算机科学与工程学院）
Beyond-Labels: Advancing Open-Vocabulary Segmentation With Vision-Language Models
Artificial Intelligence Creative Research Lab, ETRI University of Science（人工智能创意研究实验室，ETRI大学）；Field Robotics Research Section, ETRI University of Science（机器人领域研究部，ETRI大学）；Artificial Intelligence Creative Research Lab, ETRI Daejeon, South Korea（人工智能创意研究实验室，ETRI大田，韩国）
Scalable, Proposal-free Instance Segmentation Network for 3D Pixel Clustering and Particle Trajectory Reconstruction in Liquid Argon Time Projection Chambers
Stanford University（斯坦福大学）；SLAC National Accelerator Laboratory（SLAC国家加速器实验室）
Superpixel-Based QUBO for Scalable Quantum-Enhanced Medical Image Segmentation
American University of Beirut（贝鲁特美国大学）；Centre de Visió per Computador (CVC)（计算机视觉中心 (CVC)）；Ingenii Inc.（英格尼公司）；Hamad Bin Khalifa University（哈马德·本·哈利法大学）
CataractSAM-2: A Domain-Adapted Model for Anterior Segment Surgery Segmentation and Scalable Ground-Truth Annotation
Thomas Jefferson High School for Science and Technology（托马斯·杰弗逊高中科学与技术学校）；Mass Eye and Ear, Harvard Medical School（麻省眼耳研究所，哈佛医学院）；Harvard Ophthalmology AI Lab, Schepens Eye Research Institute of Mass Eye and Ear, Harvard Medical School（哈佛眼科学人工智能实验室，麻省眼耳研究所的谢普恩斯眼研究学院，哈佛医学院）
