RGBD20K is a new large-scale RGB‑D semantic segmentation dataset featuring 20,000 image pairs and 160 fine‑grained categories, surpassing existing benchmarks like NYUv2 and SUN RGB‑D in both scale and semantic diversity. The dataset provides high‑fidelity annotations obtained through rigorous re‑evaluation and correction of prior labels, ensuring a clean ground‑truth foundation. Additionally, the authors introduce a score‑purified fusion (SPF) method that achieves state‑of‑the‑art performance across evaluated benchmarks, demonstrating the value of high‑quality multimodal information.
By Shaohua Dong, Zexuan Meng, Haiyan Sun, Bing Fan, Cuicui Zhang, Dylan Joseph, Kewei Sha, Yunhe Feng, Heng Fan
RGBD20K is a new large-scale RGB‑D dataset designed to advance semantic segmentation research. It contains 20,000 image pairs annotated with 160 fine‑grained categories, far exceeding the diversity of existing benchmarks such as NYUv2 and SUN RGB‑D. The authors also provide high‑fidelity annotations and introduce a score‑purified fusion (SPF) method that achieves state‑of‑the‑art results on multiple benchmarks.
VesperMUSE is a multi‑modal vision transformer that enhances image semantic segmentation under varied lighting by combining physics‑informed low‑light adaptation, thermal‑guided low‑light image enhancement, and surface normal maps. The framework fuses RGB, thermal, and normal features through a modality‑agnostic self‑attention mechanism, enabling robust segmentation without architectural changes for different sensor setups. Extensive tests show that VesperMUSE recovers missing visual cues and achieves state‑of‑the‑art performance in extreme, unstructured environments.
By Raul Balmez
arXiv:2610.07014v1 Announce Type: new
Abstract: RGB-D semantic segmentation has made notable progress by fusing RGB and Depth, yet mainstream models still learn features almost exclusively from pixel...
By Ziang Wei, Yinlong Liu, Yan Xia, Alois Knoll, Hu Cao
The paper introduces OVRSISBench, a unified benchmark for open‑vocabulary remote sensing image segmentation, and evaluates existing OVS/OVRSIS models, uncovering their shortcomings in remote sensing contexts. Leveraging insights from this evaluation, the authors propose RSKT‑Seg, a new framework featuring a Multi‑Directional Cost Map Aggregation module, an Efficient Cost Map Fusion transformer, and a Remote Sensing Knowledge Transfer module. Experiments on the benchmark demonstrate that RSKT‑Seg outperforms strong baselines by +3.8 mIoU and +5.9 mACC while achieving twice the inference speed.
By Bingyu Li, Haocheng Dong, Da Zhang, Zhiyuan Zhao, Junyu Gao, Xuelong Li
arXiv:2602. 07343v2 Announce Type: replace-cross Abstract: Robust semantic segmentation of road scenes under adverse illumination, lighting, and shadow conditions remain a core challenge for autonomous driving applications.
By Ruturaj Reddy, Hrishav Bakul Barua, Junn Yong Loo, Thanh Thi Nguyen, Ganesh Krishnasamy