GENIE：用於神經輻射場互動編輯的高斯編碼

摘要

神經輻射場（NeRF）與高斯濺射（GS）技術近期革新了三維場景的表示與渲染方式。NeRF通過神經網絡學習體積表示，實現了高保真度的新視角合成，但其隱式編碼使得編輯與物理交互面臨挑戰。相比之下，GS將場景表示為顯式的高斯基元集合，支持實時渲染、更快的訓練速度以及更直觀的操作。這種顯式結構使GS特別適合於交互式編輯及與基於物理的模擬相結合。本文介紹了GENIE（高斯編碼用於神經輻射場交互編輯），這是一種混合模型，它結合了NeRF的逼真渲染質量與GS的可編輯結構化表示。我們並未採用球諧函數進行外觀建模，而是為每個高斯分配了一個可訓練的特徵嵌入。這些嵌入用於基於每個查詢點的k個最近高斯來條件化NeRF網絡。為了使這種條件化高效進行，我們引入了射線追蹤高斯鄰近搜索（RT-GPS），這是一種基於改進射線追蹤管線的快速最近高斯搜索方法。此外，我們還整合了多分辨率哈希網格來初始化並更新高斯特徵。這些組件共同實現了實時、局部感知的編輯：當高斯基元被重新定位或修改時，其插值影響會立即反映在渲染輸出中。通過結合隱式與顯式表示的優勢，GENIE支持直觀的場景操控、動態交互以及與物理模擬的兼容性，彌合了基於幾何的編輯與神經渲染之間的鴻溝。代碼可在(https://github.com/MikolajZielinski/genie)找到。

English

Neural Radiance Fields (NeRF) and Gaussian Splatting (GS) have recently transformed 3D scene representation and rendering. NeRF achieves high-fidelity novel view synthesis by learning volumetric representations through neural networks, but its implicit encoding makes editing and physical interaction challenging. In contrast, GS represents scenes as explicit collections of Gaussian primitives, enabling real-time rendering, faster training, and more intuitive manipulation. This explicit structure has made GS particularly well-suited for interactive editing and integration with physics-based simulation. In this paper, we introduce GENIE (Gaussian Encoding for Neural Radiance Fields Interactive Editing), a hybrid model that combines the photorealistic rendering quality of NeRF with the editable and structured representation of GS. Instead of using spherical harmonics for appearance modeling, we assign each Gaussian a trainable feature embedding. These embeddings are used to condition a NeRF network based on the k nearest Gaussians to each query point. To make this conditioning efficient, we introduce Ray-Traced Gaussian Proximity Search (RT-GPS), a fast nearest Gaussian search based on a modified ray-tracing pipeline. We also integrate a multi-resolution hash grid to initialize and update Gaussian features. Together, these components enable real-time, locality-aware editing: as Gaussian primitives are repositioned or modified, their interpolated influence is immediately reflected in the rendered output. By combining the strengths of implicit and explicit representations, GENIE supports intuitive scene manipulation, dynamic interaction, and compatibility with physical simulation, bridging the gap between geometry-based editing and neural rendering. The code can be found under (https://github.com/MikolajZielinski/genie)

GENIE：用於神經輻射場互動編輯的高斯編碼

GENIE: Gaussian Encoding for Neural Radiance Fields Interactive Editing

摘要

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