CADSplat is a framework that reconstructs photorealistic, geometrically accurate digital twins from fewer than 15 wide‑baseline images by regularizing 3D Gaussian Splatting with an explicit CAD shape prior. It matches segmented object silhouettes to a CAD library to retrieve a suitable model and camera poses, then anchors Gaussian primitives to the model’s surface and jointly optimizes splat parameters, registration, and a non‑rigid deformation field. Experiments on two real‑world datasets show CADSplat outperforms baselines, especially in sparse and self‑occluded scenarios, and its gains mainly stem from constraining splats to a surface rather than the CAD shape itself.
By Kristof Overdulve, Lode Jorissen, Nick Michiels
arXiv:2609.18037v1 Announce Type: new
Abstract: Segment Anything Models provide reusable priors, yet they require user prompts and cannot support fully automatic instrument segmentation. Automatic pr...
By Dawei Yan, Yuezhe Yang, Menglan Ruan, Chunfeng Yang, Yudong Zhang
arXiv:2609.19042v1 Announce Type: cross
Abstract: In medical imaging, developing robust deep learning models requires data from various domains. However, regulatory policies protecting patient privac...
By Karan R. Bagri, Tarun K. Garg, Vaanathi Sundaresan
arXiv:2609.07939v2 Announce Type: replace
Abstract: We address the problem of discovering repeated elements from a single image. In contrast to existing approaches that depend on large annotated data...
By Syrine Kalleli, Alexei A. Efros, Mathieu Aubry
PDA++ is a unified, environment‑aware object insertion framework for remote sensing imagery that improves few‑shot and long‑tail recognition. It operates in three stages: Planning, which selects scene‑compatible poses using an affordance field; Decoupling, which conditions the background on pose to preserve object identity while adapting to the scene; and Assimilation, which aligns multi‑scale texture distributions via optimal transport to enhance local coherence. The method achieves a whole‑image FID of 6.28 and boosts average few‑shot recognition mAP50 by 17.69 points on optical data, while also improving ship detection on SAR imagery and maintaining performance under cross‑dataset transfer and amorphous‑target insertion.
By Xianchi Dong, Yingyan Hou, Chao Ren, Wanxuan Lu, Zihan Wei, Hongfeng Yu, Yixiao Wang, Chubo Deng, Xian Sun
PANORAMA introduces a new panoptic grounded captioning framework that jointly generates detailed image captions and associates each phrase with precise pixel-level masks. The authors create PanoCaps, a human‑annotated benchmark with dense captions and near‑complete pixel coverage, and propose a phrase‑mask matching protocol with a generalized Panoptic Quality metric. PANORAMA conditions a pretrained segmenter on contextualized phrase representations, learns to select appropriate masks, and achieves state‑of‑the‑art grounding performance on PanoCaps and other pixel‑level tasks.
By Sara Pieri, Evangelos Kazakos, Shizhe Chen, Josef Sivic, Cordelia Schmid
The paper presents CapMap-MS-TTA, a unified multimodal model that achieved third place in the MUMU track of the 8th Large-scale Video Object Segmentation Challenge. The solution tackles image tagging, open‑vocabulary object detection, and English captioning under strict resource limits, using an expanded keyword lexicon and lightweight expand‑hints for tagging, and Florence‑2 with multi‑scale and horizontal‑flip TTA plus label‑aware NMS for detection. The approach improves the reproduced Florence‑2 baseline from 15.16 to a best public score of 16.4815 without fine‑tuning.
By Chengfeng Qiu, Kaifeng Wei
FedPGT introduces a progressive gradient transmission scheme for vehicular federated learning over time‑varying channels, where vehicles send high‑magnitude gradient entries according to instantaneous channel conditions. The authors derive a convergence bound showing diminishing returns governed by a power‑law decay, and formulate a stochastic optimization problem that is solved via a Lyapunov drift‑plus‑penalty approach with per‑slot surrogate variables. A low‑complexity resource allocation algorithm is proposed, and experiments on CIFAR‑10 and Argoverse demonstrate a 3.65% accuracy gain and a 12.66% reduction in displacement error compared to state‑of‑the‑art baselines.
By Jintao Yan, Tan Chen, Yuxuan Sun, Sheng Zhou, Zhisheng Niu
arXiv:2609.06074v2 Announce Type: replace
Abstract: Sparse optical flow provides stable inter-frame correspondence, playing a key role in Visual Odometry (VO) and Visual-Inertial Odometry (VIO). Clas...
By Yicheng Lin, Zhipeng Fei, Yuxiu Xu, WenDong Chen, Cong Li, Bin Han
arXiv:2609.18773v1 Announce Type: new
Abstract: Long-range infrared imaging frequently confronts dense target clusters whose diffraction-limited signatures merge into a single indistinguishable blob,...
By Mengze Xu, Zhu Liu, Weidong Sheng, Boyang Li, Yimian Dai, Ming-Ming Cheng, Jian Yang
TwinMark is a watermarking scheme that embeds a single SHAKE128 secret into a vision model using two complementary linear functionals of model-output summaries: a covariance projector (cov‑Feat) and a class‑conditional Fisher‑aligned linear carrier (cc‑FALC). These readouts cover both classifier APIs attacked by KL knowledge distillation and representation‑only hosts attacked by feature‑matching distillation, each providing a teacher‑measurable a posteriori certificate. Across 13 attacks on datasets such as CIFAR‑10, CIFAR‑100, and Mini‑ImageNet, TwinMark remains detectable on every post‑attack model that retains task utility, survives cross‑architecture distillation onto ResNet‑18/50, VGG‑16, and MobileNet‑V3, and can be ported to GNSS few‑shot, VOC detection, ISIC segmentation, and STL‑10 SimCLR.
By Redwanul Karim, Tobias Feigl, Christopher Mutschler, Felix Ott
Visual Perception Engine (VPEngine) is a modular framework that enables efficient GPU usage for robotic vision tasks by sharing a foundation model backbone across multiple specialized task heads. It eliminates redundant feature extraction, supports dynamic task prioritization, and achieves up to 3× speedup over sequential execution. The open‑source Python implementation, with ROS2 C++ bindings, delivers real‑time performance (≥50 Hz) on NVIDIA Jetson Orin AGX using TensorRT‑optimized models.
By Jakub {\L}ucki, Jonathan Becktor, Georgios Georgakis, Rob Royce, Shehryar Khattak
This paper provides a detailed overview of the Ultralytics YOLO family from YOLOv5 to YOLO27, highlighting key architectural changes, benchmarking results, and deployment considerations. It discusses the evolution of each version—YOLO27’s scale‑adaptive dual architecture, YOLO26’s loss and optimization refinements, YOLO11’s efficiency focus, YOLOv8’s anchor‑free detection, and YOLOv5’s modular ecosystem—alongside performance metrics on COCO and latency on TensorRT. The review also surveys applications in robotics, agriculture, surveillance, and manufacturing, and outlines future challenges such as dense scene handling, CNN‑Transformer integration, and hardware‑aware optimization.
By Ranjan Sapkota, Manoj Karkee
arXiv:2609.18444v1 Announce Type: new
Abstract: Weakly supervised class-incremental semantic segmentation (WILSS) aims to train a segmentation model over multiple steps, each introducing new concepts...
By Leon Arthur Marx, Francesco Barbato, Matteo Caligiuri, Pietro Zanuttigh
arXiv:2609.17749v1 Announce Type: new
Abstract: A lot of expectations have been put for years on integrating domain expert knowledge in image classification models. Several approaches have been explo...
By Dieu-Donn\'e Fangnon, Diane Lingrand, Aur\'elie Liard, Marco Corneli, Antoine Pasqualini, Fr\'ed\'eric Precioso
arXiv:2609.18493v1 Announce Type: new
Abstract: Semantic labels for indoor mobile laser scanning (MLS) frames remain largely absent from current point cloud semantic segmentation benchmarks, which ma...
By Haiyang Wu, Muhammad Affan, George Vosselman, Ville Lehtola
WARD is a runtime‑adaptive Vision Transformer framework designed for edge AI that combines channel‑wise subnetwork partitioning, reliability‑aware continual learning, and dynamic operating‑mode scheduling. It operates two physically isolated subnetworks across four modes—Full‑Precision, Low‑Power, High‑Reliability, and Adaptive—to balance computational cost and fault tolerance while maintaining uninterrupted inference. Implemented on a lightweight FPGA accelerator with minimal area overhead, WARD achieves a network‑level failure rate of 1.79% under high Bit Error Rates and supports rapid mode transitions within a few clock cycles.
By Mahdi Taheri, Pramit Kumar Bhaduri, Mohammad Masoumi, Ali Mahani
The paper introduces LDE, a framework that uses collaborative perception (CP) to generate high‑quality pseudo‑labels for unsupervised model adaptation in autonomous driving. It tackles communication limits, field‑of‑view mismatches, and label unreliability through selective feature sharing, FoV filtering, and curriculum learning. Experiments on 3D object detection show LDE surpasses pre‑trained models and existing adaptation methods.
By Yanan Ma, Yihang Tao, Zhengru Fang, Zihan Fang, Yiqin Deng, Xianhao Chen, Yuguang Fang
arXiv:2609.18542v1 Announce Type: new
Abstract: 4D radar has emerged as a promising next-generation sensor for improving the robustness of autonomous driving perception systems because of its stable...
By Woo-Jin Jung, Dong-Hee Paek, Jeong-Su Park, Seung-Hyun Kong
arXiv:2606.31679v2 Announce Type: replace-cross
Abstract: Mounted butterflies are among the most striking objects in natural history collections. However, their beauty is notoriously hard to digitize...
By Kristof Overdulve, Lode Jorissen, Nick Michiels