<?xml version="1.0" encoding="utf-8" standalone="yes"?><rss version="2.0" xmlns:atom="http://www.w3.org/2005/Atom"><channel><title>Zhe Liu | RCL Group</title><link>https://rcl-group.github.io/authors/zhe-liu/</link><atom:link href="https://rcl-group.github.io/authors/zhe-liu/index.xml" rel="self" type="application/rss+xml"/><description>Zhe Liu</description><generator>Hugo Blox Builder (https://hugoblox.com)</generator><language>en-us</language><lastBuildDate>Wed, 03 Sep 2025 00:00:00 +0000</lastBuildDate><image><url>https://rcl-group.github.io/media/icon_hu_c9b5ec8305c0ee34.png</url><title>Zhe Liu</title><link>https://rcl-group.github.io/authors/zhe-liu/</link></image><item><title>PG-SLAM Accepted by T-RO</title><link>https://rcl-group.github.io/blog/2025-pgslam-tro/</link><pubDate>Wed, 03 Sep 2025 00:00:00 +0000</pubDate><guid>https://rcl-group.github.io/blog/2025-pgslam-tro/</guid><description>&lt;p&gt;Our work
has been accepted by IEEE Transactions on Robotics (T-RO)! PG-SLAM is a photo-realistic and geometry-aware RGB-D SLAM method for dynamic environments built upon Gaussian Splatting. It jointly maps the dynamic foreground (including non-rigid humans and rigid items), reconstructs the static background, and localizes the camera by leveraging both geometric and appearance constraints. Experiments on various real-world datasets demonstrate state-of-the-art performance in both camera localization and scene representation.&lt;/p&gt;</description></item></channel></rss>