added rayfronts arxiv to publications and rayfronts website to research page

Author: seungchan-kim

_bibliography/references.bib

@@ -1,3 +1,14 @@
+@misc{alama2025rayfronts,
+      title={RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration}, 
+      author={Omar Alama and Avigyan Bhattacharya and Haoyang He and Seungchan Kim and Yuheng Qiu and Wenshan Wang and Cherie Ho and Nikhil Keetha and Sebastian Scherer},
+      year={2025},
+      eprint={2504.06994},
+      archivePrefix={arXiv},
+      primaryClass={cs.RO},
+      url={https://arxiv.org/pdf/2504.06994},
+	  video={https://youtu.be/fFSKUBHx5gA},
+	  abstract={Open-set semantic mapping is crucial for open-world robots. Current mapping approaches either are limited by the depth range or only map beyond-range entities in constrained settings, where overall they fail to combine within-range and beyond-range observations. Furthermore, these methods make a trade-off between fine-grained semantics and efficiency. We introduce RayFronts, a unified representation that enables both dense and beyond-range efficient semantic mapping. RayFronts encodes task-agnostic open-set semantics to both in-range voxels and beyond-range rays encoded at map boundaries, empowering the robot to reduce search volumes significantly and make informed decisions both within & beyond sensory range, while running at 8.84 Hz on an Orin AGX. Benchmarking the within-range semantics shows that RayFronts's fine-grained image encoding provides 1.34x zero-shot 3D semantic segmentation performance while improving throughput by 16.5x. Traditionally, online mapping performance is entangled with other system components, complicating evaluation. We propose a planner-agnostic evaluation framework that captures the utility for online beyond-range search and exploration, and show RayFronts reduces search volume 2.2x more efficiently than the closest online baselines.}
+}
 @misc{baek2025pipe,
       title={PIPE Planner: Pathwise Information Gain with Map Predictions for Indoor Robot Exploration}, 
       author={Seungjae Baek and Brady Moon and Seungchan Kim and Muqing Cao and Cherie Ho and Sebastian Scherer and Jeonghwan Jeon},

_posts/2025-04-10-rayfronts.md

@@ -0,0 +1,23 @@
+---
+layout: post
+title: "RayFronts: Open-Set Semantic Ray Frontiers for Online Scene Understanding and Exploration"
+date: 2025-04-10 10:33:01
+categories: research
+description: "RayFronts is a real-time semantic mapping system that enables fine-grained scene understanding both within and beyond the depth perception range allowing robots to localize and effectively limit search volumes."
+author: "Seungchan Kim"
+published: true
+redirect: "https://rayfronts.github.io/"
+show_sidebar: false
+# slim_content_width: true
+permalink: /rayfronts/
+image: /img/posts/2025-04-10-rayfronts/rayfronts-teaser.gif
+datatable: true
+title_image: None
+hero_image: /img/posts/2025-04-10-rayfronts/rayfronts-teaser.gif
+hero_height: is-large
+remove_hero_title: false
+menubar_toc: false
+tags: Perception, Learning
+---
+
+RayFronts is a real-time semantic mapping system that enables fine-grained scene understanding both within and beyond the depth perception range allowing robots to localize and effectively limit search volumes. RayFronts can be queried with open-set images and text within the map and beyond it.