A real-time robot-centric video world model driven by depth-aware hand skeletons generates imitation-learning trajectories that support zero-shot real-robot transfer and improve policies when mixed with real data.
Hfgs: 4d gaussian splatting with emphasis on spatial and temporal high-frequency components
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abstract
Robot-assisted minimally invasive surgery benefits from enhancing dynamic scene reconstruction, as it improves surgical outcomes. While Neural Radiance Fields (NeRF) have been effective in scene reconstruction, their slow inference speeds and lengthy training durations limit their applicability. To overcome these limitations, 3D Gaussian Splatting (3D-GS) based methods have emerged as a recent trend, offering rapid inference capabilities and superior 3D quality. However, these methods still struggle with under-reconstruction in both static and dynamic scenes. In this paper, we propose HFGS, a novel approach for deformable endoscopic reconstruction that addresses these challenges from spatial and temporal frequency perspectives. Our approach incorporates deformation fields to better handle dynamic scenes and introduces Spatial High-Frequency Emphasis Reconstruction (SHF) to minimize discrepancies in spatial frequency spectra between the rendered image and its ground truth. Additionally, we introduce Temporal High-Frequency Emphasis Reconstruction (THF) to enhance dynamic awareness in neural rendering by leveraging flow priors, focusing optimization on motion-intensive parts. Extensive experiments on two widely used benchmarks demonstrate that HFGS achieves superior rendering quality.
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G-SHARP is a Gaussian splatting pipeline for real-time deformable tissue reconstruction in surgery, built on the open GSplat rasterizer and deployed via Holoscan on NVIDIA edge hardware with claimed state-of-the-art results on the EndoNeRF benchmark.
A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.
citing papers explorer
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RynnWorld-Teleop: An Action-Conditioned World Model for Digital Teleoperation
A real-time robot-centric video world model driven by depth-aware hand skeletons generates imitation-learning trajectories that support zero-shot real-robot transfer and improve policies when mixed with real data.
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G-SHARP: Gaussian Surgical Hardware Accelerated Real-time Pipeline
G-SHARP is a Gaussian splatting pipeline for real-time deformable tissue reconstruction in surgery, built on the open GSplat rasterizer and deployed via Holoscan on NVIDIA edge hardware with claimed state-of-the-art results on the EndoNeRF benchmark.
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A Survey on 3D Gaussian Splatting
A survey compiling principles, applications, benchmarks, and challenges of 3D Gaussian Splatting for explicit 3D scene representation.