COLOR-NEUS: Reconstructing Neural Implicit Surfaces with Color

Abstract

COLOR-NEUS aims to reconstruct high-fidelity neural implicit surfaces enriched with color information, leveraging advancements in neural rendering and implicit surface representation. By integrating geometric detail and color properties, the project seeks to achieve accurate and photorealistic 3D reconstructions suitable for applications in graphics, virtual reality, and digital twin creation. The model incorporates color consistency and surface regularization to produce visually and geometrically coherent results.

Existing System

Existing 3D reconstruction systems often separate geometry reconstruction and color mapping processes, resulting in less coherent outputs. These systems also struggle with maintaining accuracy in detailed surface representation while preserving color fidelity.

Proposed System

COLOR-NEUS introduces an integrated framework for simultaneous surface reconstruction and colorization using neural implicit surfaces. The system employs a combination of signed distance fields (SDFs) and color features to create accurate and visually appealing reconstructions. It ensures smooth surface transitions while maintaining consistent color properties, even in complex scenes.

Module Description

1. Data Acquisition Module: Captures input data, including multi-view images and depth maps, for training.
2. Preprocessing Module: Normalizes and augments input data to improve training robustness.
3. Neural Network Module: Trains implicit surface networks with a dual focus on geometry and color features.
4. Rendering Module: Produces high-quality visualizations of reconstructed surfaces with realistic color.
5. Evaluation Module: Compares reconstructed models with ground truth using metrics for geometry and color fidelity.

Software and Hardware Requirements

Software:

• Programming Languages: Python, C++
• Libraries/Frameworks: PyTorch, TensorFlow (for neural network training); OpenCV (for image processing); Blender or similar for visualization.
• Development Tools: Jupyter Notebook, Visual Studio Code
• Rendering Tools: NVIDIA OptiX or similar for GPU-accelerated rendering.

Hardware:

• GPU: High-performance NVIDIA GPUs (RTX 3090 or higher) for rendering and training.
• CPU: Multi-core processor for efficient parallel data processing.
• RAM: At least 32 GB for handling complex 3D data and models.
• Storage: High-capacity SSDs for managing large datasets and model checkpoints.

Functional Requirements

• High Accuracy in Geometry: Produce surfaces with detailed and accurate geometric features.
• Color Consistency: Ensure color information is accurately reconstructed and seamlessly integrated with surfaces.
• Efficient Rendering: Generate reconstructions quickly without compromising quality.
• Scalability: Handle increasing complexity and size of input datasets.

Non-Functional Requirements

• Performance: Optimized to minimize training and rendering times.
• Reliability: Consistently produce high-quality results across diverse datasets.
• Usability: Offer an intuitive interface for researchers and developers to interact with the system.
• Maintainability: Easily adaptable to incorporate new techniques or features.