FoRIS is a training‑free in‑context segmentation framework that refines foreground masks through a coarse‑to‑fine process. It operates in three stages—Foreground Purification, Localization, and Consolidation—to suppress background noise, pinpoint target regions, and reconstruct complete foreground structures. The method achieves state‑of‑the‑art performance, improving mIoU by 4.5 and 4.8 points in 1‑shot and 5‑shot settings respectively.
By Ming Hu, Jianfu Yin, Mingyu Dou, Miaomiao Zhang, Yao Wang, Cong Hu, Bingliang Hu, Quan Wang
The paper introduces NAIMA, a guided depth super‑resolution framework that leverages global contextual semantic priors from pretrained vision transformer token embeddings. Its Guided Token Attention (GTA) module uses depth encodings as queries to attend over semantic tokens, with a zero‑initialized gate controlling the influence of semantic evidence. NAIMA achieves competitive in‑distribution performance while delivering superior cross‑dataset generalization without relying on decoded priors or auxiliary objectives.
By Tayyab Nasir, Daochang Liu, Ajmal Mian
arXiv:2608.21710v1 Announce Type: new
Abstract: With the advance of deep neural networks, the quality of disparity maps obtained through stereo matching has steadily improved. However, existing stere...
By Bohan Li
arXiv:2509.04859v4 Announce Type: replace
Abstract: Fast and efficient photorealistic 3D reconstruction with (semantic) Gaussian Splatting (GS) is crucial for time-critical robotic perception and nav...
By Hannah Schieber, Dominik Frischmann, Victor Schaack, Simon Boche, Angela Schoellig, Stefan Leutenegger, Daniel Roth
U-Net remains a practical baseline for image segmentation because of its simple encoder-decoder structure and skip connections. However, the bottleneck representation is still dominated by a limited s...
arXiv:2609.10387v1 Announce Type: new
Abstract: Deformable convolution networks have recently become popular for many computer vision tasks, especially for semantic segmentation, because of their exc...
By Yixiao Li, Xiaoyuan Yang, Jin Jiang, Minghao Zou, Guanghui Yue, Baoquan Zhao, Jun Liu, Wei Zhou
arXiv:2609.22834v1 Announce Type: new
Abstract: Open-vocabulary semantic segmentation (OVS) of remote sensing imagery is a challenging pixel-level task requiring strong generalization and adaptation...
By Changhao Zhao, Linglin Zeng, Hai Liu
arXiv:2606.08866v2 Announce Type: replace
Abstract: CNN-based semantic segmentation networks usually rely on context heads such as ASPP, PPM, or attention modules to enlarge the receptive field. Thes...
By Hsin-Jui Pan, Sheng-Wei Chan, Chun-Po Shen, Jen-Shiun Chiang
arXiv:2607. 16012v1 Announce Type: cross Abstract: Multi-Task Learning (MTL) in robotics perception systems supports comprehensive 3D spatial scene understanding by integrating semantic segmentation and depth estimation.
By Jehun Kang, Jungha Wang, Youngjun Hwang, David Hyunchul Shim
Semantic segmentation of remote sensing imagery requires models that capture both global context and local detail under tight computational budgets. Prior work typically optimizes for one of these axes: attention for global context, convolution for local detail, or compactness for efficiency.
Hierarchical 3D scene graphs are a promising representation for high-level spatial reasoning in autonomous mobile platforms. However, existing extraction frameworks typically rely on purely local visual clustering or strict geometric heuristics, such as wall-separated rooms, which fail in open-plan or arbitrarily-structured environments.
The paper introduces a privacy‑preserving approach for semantic segmentation that fuses high‑resolution depth with ultra‑low‑resolution RGB images. A joint 2D framework uses depth to guide RGB reconstruction and RGB‑D segmentation, while an end‑to‑end 2D‑to‑3D pipeline consolidates 2D features for 3D segmentation. Experiments on ScanNet demonstrate superior 2D and 3D performance compared to other privacy‑preserving methods, strong zero‑shot transfer to SUN RGB‑D and SceneNN, and reduced recoverability of sensitive data, with real‑robot tests showing effective object‑goal navigation.
By Xuying Huang, Swithinraj Moses Daniel, Sicong Pan, Sebastian Houben, Maren Bennewitz