This paper proposes NeuralEditor that enables neural radiance fields (NeRFs) natively editable for general shape editing tasks. Despite their impressive results on novel-view synthesis, it remains a fundamental challenge for NeRFs to edit the shape of the scene. Our key insight is to exploit the explicit point cloud representation as the underlying structure to construct NeRFs, inspired by the intuitive interpretation of NeRF rendering as a process that projects or "plots" the associated 3D point cloud to a 2D image plane. To this end, NeuralEditor introduces a novel rendering scheme based on deterministic integration within K-D tree-guided density-adaptive voxels, which produces both high-quality rendering results and precise point clouds through optimization. NeuralEditor then performs shape editing via mapping associated points between point clouds. Extensive evaluation shows that NeuralEditor achieves state-of-the-art performance in both shape deformation and scene morphing tasks. Notably, NeuralEditor supports both zero-shot inference and further fine-tuning over the edited scene. Our code, benchmark, and demo video are available at https://immortalco.github.io/NeuralEditor.

翻译：本文提出NeuralEditor，使神经辐射场（NeRFs）具备原生可编辑性，以完成通用形状编辑任务。尽管NeRFs在新视角合成任务中表现卓越，但场景形状编辑仍是基础性挑战。我们的核心见解是：受NeRF渲染可直观理解为将关联三维点云“投射”或“绘制”到二维图像平面的过程启发，利用显式点云表示作为底层结构来构建NeRFs。为此，NeuralEditor提出了一种基于K-D树引导的密度自适应体素内确定性积分的新型渲染方案，通过优化同时生成高质量渲染结果与精确点云。随后，NeuralEditor通过映射点云间的关联点实现形状编辑。广泛评估表明，NeuralEditor在形状形变与场景渐变任务中均达到业界领先水平。值得注意的是，NeuralEditor同时支持零样本推理与对编辑后场景的进一步微调。我们的代码、基准测试及演示视频已开源至https://immortalco.github.io/NeuralEditor。