CEI-3D: Collaborative Explicit-Implicit 3D Reconstruction for Realistic and Fine-Grained Object Editing

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$8,000
GPU Compute
$800
SaaS Stack
$300
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$100

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0.5-1x

3yr ROI

6-15x

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Talent Scout

B

Bingbing Ni

Shanghai Jiao Tong University

Y

Yue Shi

Shanghai Jiao Tong University

R

Rui Shi

Shanghai Jiao Tong University

Y

Yuxuan Xiong

Shanghai Jiao Tong University

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References (59)

[1]
Hunyuan3D 2.0: Scaling Diffusion Models for High Resolution Textured 3D Assets Generation
2025Zibo Zhao, Zeqiang Lai et al.
[2]
ReCap: Better Gaussian Relighting with Cross-Environment Captures
2024Jingzhi Li, Zongwei Wu et al.
[3]
Structured 3D Latents for Scalable and Versatile 3D Generation
2024Jianfeng Xiang, Zelong Lv et al.
[4]
Geometric implicit neural representations for signed distance functions
2024L. Schirmer, Tiago Novello et al.
[5]
DeferredGS: Decoupled and Relightable Gaussian Splatting With Deferred Shading
2024Tong Wu, Jiali Sun et al.
[6]
InstantMesh: Efficient 3D Mesh Generation from a Single Image with Sparse-view Large Reconstruction Models
2024Jiale Xu, Weihao Cheng et al.
[7]
GaussianEditor: Swift and Controllable 3D Editing with Gaussian Splatting
2023Yiwen Chen, Zilong Chen et al.
[8]
Implicit Shape and Appearance Priors for Few-Shot Full Head Reconstruction
2023Pol Caselles, Eduard Ramon et al.
[9]
ICE-NeRF: Interactive Color Editing of NeRFs via Decomposition-Aware Weight Optimization
2023Jaehoo Lee, Dae-shik Kim
[10]
Patch-Grid: An Efficient and Feature-Preserving Neural Implicit Surface Representation
2023Guying Lin, Lei Yang et al.
[11]
FocalDreamer: Text-driven 3D Editing via Focal-fusion Assembly
2023Yuhan Li, Yishun Dou et al.
[12]
3D Gaussian Splatting for Real-Time Radiance Field Rendering
2023Bernhard Kerbl, Georgios Kopanas et al.
[13]
DreamEditor: Text-Driven 3D Scene Editing with Neural Fields
2023Jingyu Zhuang, Chen Wang et al.
[14]
NeuralEditor: Editing Neural Radiance Fields via Manipulating Point Clouds
2023Jun-Kun Chen, Jipeng Lyu et al.
[15]
Segment Anything
2023A. Kirillov, Eric Mintun et al.
[16]
NeILF++: Inter-Reflectable Light Fields for Geometry and Material Estimation
2023Jingyang Zhang, Yao Yao et al.
[17]
SINE: Semantic-driven Image-based NeRF Editing with Prior-guided Editing Field
2023Chong Bao, Yinda Zhang et al.
[18]
I2-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs
2023Jingsen Zhu, Yuchi Huo et al.
[19]
Deep learning-based 3D reconstruction: a survey
2023Taha Samavati, M. Soryani
[20]
PaletteNeRF: Palette-based Appearance Editing of Neural Radiance Fields
2022Zhengfei Kuang, Fujun Luan et al.

Showing 20 of 59 references

Founder's Pitch

"CEI-3D provides realistic and fine-grained 3D object editing using collaborative explicit-implicit reconstruction."

3D ReconstructionScore: 9View PDF ↗

Commercial Viability Breakdown

0-10 scale

High Potential

2/4 signals

5

Quick Build

2/4 signals

5

Series A Potential

4/4 signals

10

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Why It Matters

The paper addresses a significant gap in the 3D editing landscape by offering a method that allows for both realistic rendering and precise editing capabilities, enabling applications ranging from creative content generation to virtual reality modeling and gaming.

Product Angle

To productize CEI-3D, build a SaaS platform targeting industries that use 3D modeling and customization, such as gaming, VR, and e-commerce, providing a cloud-based solution for rapid and realistic custom edits to 3D models.

Disruption

CEI-3D could replace or greatly improve existing 3D editing tools that are less flexible or require more manual input, offering a user-friendly interface with more precise control over 3D modifications.

Product Opportunity

The 3D modeling and rendering market is valued in billions, with growing demand in AR/VR, gaming, and online retail. Companies in these sectors could pay for tools that allow fast, realistic 3D model customization.

Use Case Idea

Interactive 3D object customization tool for e-commerce platforms, allowing users to visually modify product models before purchase.

Science

CEI-3D combines implicit Signed Distance Function (SDF) networks with explicit handler points to enable localized control and mutual guidance in 3D object editing. By disentangling physical properties from color and using a spatial-aware editing module, it achieves fine-grained and realistic edits more efficiently than traditional methods.

Method & Eval

The method utilizes collaborative explicit-implicit representation with an implicit SDF network and handler points, tested on real and synthetic datasets, showing superior realism and control in 3D editing over state-of-the-art methods with less time consumption.

Caveats

The technology might struggle with highly complex objects due to computational limits, could require significant user input for precise edits, and may face challenges in integration with existing 3D workflows and formats.

Author Intelligence

Bingbing Ni

LEAD
Shanghai Jiao Tong University
nibingbing@sjtu.edu.cn

Yue Shi

Shanghai Jiao Tong University
shiyue001@sjtu.edu.cn

Rui Shi

Shanghai Jiao Tong University
shi-rui@sjtu.edu.cn

Yuxuan Xiong

Shanghai Jiao Tong University
xiongyx@sjtu.edu.cn

Wenjun Zhang

Shanghai Jiao Tong University
zhangwenjun@sjtu.edu.cn

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