Los Angeles, California, Oct. 29, 2025 (GLOBE NEWSWIRE) -- Linkhome Holdings Inc. (Nasdaq: LHAI), a leading AI-powered real estate technology company, today announced that its wholly owned subsidiary, Linkhome AI, Inc., has entered into a strategic partnership agreement with Beike Realsee Technology (HK) Limited, one of China’s largest and most advanced real estate technology platforms and the backbone of KE Holdings Inc. (NYSE: BEKE)’s 3D virtual-tour property viewing solution. Under the agreement, the two ...

Core Viewpoint - The research introduces a novel method called "Shape of Motion" that combines 3D Gaussian point technology with SE(3) motion representation, achieving a 73% improvement in 3D tracking accuracy compared to existing methods, with significant applications in AR/VR and autonomous driving [2][4]. Summary by Sections Introduction - The challenge of dynamic scene reconstruction from monocular video is likened to feeling an elephant in the dark due to the lack of information [7]. - Traditional methods rely on multi-view videos or depth sensors, making them less effective for dynamic scenes [7]. Core Contribution - The "Shape of Motion" technique enables the reconstruction of complete 4D scenes (3D space + time) from a single video, allowing for the tracking of object motion and rendering from any viewpoint [9][10]. - Two main innovations include low-dimensional motion representation using SE(3) motion bases and the integration of data-driven priors for a globally consistent dynamic scene representation [9][12]. Technical Analysis - The method employs 3D Gaussian points as the basic unit for scene representation, allowing for real-time rendering [10]. - Various data-driven priors, such as monocular depth estimation and long-range 2D trajectories, are utilized to overcome the under-constrained nature of monocular video reconstruction [11][12]. Experimental Results - The method outperforms existing techniques on the iPhone dataset, achieving a 73.3% accuracy in 3D tracking and a PSNR of 16.72 for new view synthesis [17][18]. - The 3D tracking error (EPE) is reported as low as 0.16 on the Kubric synthetic dataset, showing a 21% improvement over baseline methods [20]. Discussion and Future Outlook - The current method faces challenges such as training time and reliance on accurate camera pose estimation [25]. - Future directions include optimizing training time, enhancing view generation capabilities, and developing fully automated segmentation methods [25]. Conclusion - The "Shape of Motion" research marks a significant advancement in monocular dynamic reconstruction, with potential applications in real-time tracking for AR glasses and autonomous systems [26].