Research on Efficient Fine-Grained 3D Scene Editing Based on Rotation-Invariant Neural Point Fields

Academic Background

In the fields of computer vision and graphics, modeling and rendering novel views of real scenes from multi-view images is a central problem. Neural Radiance Fields (NeRF) have recently demonstrated significant potential in generating high-quality novel view synthesis results and are considered a promising alternative to traditional explicit 3D representations such as meshes or voxels. However, despite NeRF’s impressive rendering quality, its capabilities in scene editing remain limited. Existing editable NeRF methods exhibit notable shortcomings in efficiency and fine-grained editing capabilities, which hinder NeRF’s potential in creative editing and practical applications.

To address this issue, researchers have proposed an editing framework based on Rotation-Invariant Neural Point Fields, aiming to achieve efficient and fine-grained 3D scene editing by leveraging the complementary strengths of implicit NeRF representations and explicit point-based representations. This research not only enhances the rendering quality after editing but also introduces a multi-view ensemble learning strategy to enable real-time interactive segmentation from 2D images to 3D neural point fields, thereby simplifying the user workflow.

Source of the Paper

This paper is a collaborative effort by researchers from multiple universities in China. The primary authors include Yuze Wang, Junyi Wang, Chen Wang, and Yue Qi. Among them, Yuze Wang and Yue Qi are affiliated with the State Key Laboratory of Virtual Reality Technology and Systems at Beihang University, Junyi Wang is from the School of Computer Science and Technology at Shandong University, and Chen Wang is from the School of Computer Science and Engineering at Beijing Technology and Business University. The paper was published in 2025 in the journal Neural Networks, titled “RISE-Editing: Rotation-Invariant Neural Point Fields with Interactive Segmentation for Fine-Grained and Efficient Editing.”

Research Process

1. Rotation-Invariant Neural Point Field Representation

The study first proposes a rotation-invariant neural point field representation method, aiming to improve scene rendering quality after fine-grained editing by learning local content using Cartesian coordinates. This method designs a Rotation-Invariant Neural Inverse Distance Weighted Interpolation (RNIDWI) module to effectively aggregate neural points, ensuring the preservation of view-dependent features during editing.

2. Multi-View Ensemble Learning Strategy

To achieve efficient interactive editing, the research team introduces a multi-view ensemble learning strategy that lifts inconsistent 2D zero-shot segmentation results to 3D neural point fields in real time. Users can efficiently segment 3D neural point fields and manipulate corresponding neural points by simply clicking on 2D images, enabling fine-grained editing of implicit fields.

3. Cross-Scene Rendering Module

To enhance the efficiency of cross-scene compositing, the study disentangles the traditional NeRF representation into a scene-agnostic rendering module and scene-specific neural point fields. This approach not only reduces time and space requirements but also supports complex cross-scene interactions.

4. Experimental Results and Evaluation

The study conducts experiments on multiple public datasets, including the NeRF synthetic dataset, ScanNet dataset, and NeRF segmentation benchmark dataset. The results demonstrate that the method outperforms existing approaches in editing capabilities, rendering quality, and space-time efficiency. Specifically, the study showcases various editing functions, such as part duplication, scaling, transformation, deletion, and cross-scene compositing, and generates high-quality novel view synthesis results.

Key Findings

1. Enhanced Editing Capabilities

Through the rotation-invariant neural point field representation, the study significantly improves rendering quality after editing. Experiments show that the method maintains detail integrity when editing complex scenes, such as plant leaves, avoiding common rendering artifacts in traditional methods.

2. Efficient Interactive Editing

The multi-view ensemble learning strategy enables users to segment and edit 3D neural point fields in real time through simple click operations. Compared to existing methods, this approach significantly enhances editing efficiency and user-friendliness.

3. Cross-Scene Compositing

By disentangling the scene-agnostic rendering module and scene-specific neural point fields, the study achieves efficient cross-scene compositing. Experiments demonstrate the editing and compositing results of multiple scenes, highlighting the method’s flexibility and generality.

Conclusion and Significance

The core contribution of this research lies in proposing an efficient and fine-grained 3D scene editing framework. By introducing rotation-invariant neural point field representation and a multi-view ensemble learning strategy, the study significantly enhances editing capabilities and rendering quality. This method not only simplifies the user workflow but also opens new possibilities for creative 3D content editing, with broad application prospects in fields such as virtual reality and film production.

Research Highlights

1. Rotation-Invariant Neural Point Field Representation: Ensures rendering quality after editing by introducing rotation-invariant constraints.
2. Multi-View Ensemble Learning Strategy: Enables real-time interactive segmentation from 2D images to 3D neural point fields, improving editing efficiency.
3. Cross-Scene Rendering Module: Supports efficient cross-scene compositing by disentangling the scene-agnostic rendering module.
4. Extensive Experimental Validation: Validates the method’s effectiveness on multiple public datasets, demonstrating its advantages in editing capabilities and rendering quality.

Additional Valuable Information

Despite the significant progress in editing capabilities and efficiency, the method still has some limitations. For example, it relies on the accuracy of 2D segmentation models, which may perform poorly on very fine targets. Additionally, the method does not model lighting effects, making it challenging to generate realistic reflections and shadows in certain lighting environments. Future research could consider integrating more advanced interactive segmentation methods and NeRF relighting techniques to further enhance editing results.

This study provides new ideas and methods for the field of 3D scene editing, offering significant scientific value and application potential.