BAA-NGP: BUNDLE-ADJUSTING ACCELERATED NEURAL GRAPHICS PRIMITIVES

Abstract

The project “BAA-NGP: Bundle-Adjusting Accelerated Neural Graphics Primitives” introduces an innovative framework combining the concepts of bundle adjustment and neural graphics primitives to optimize visual data representation and rendering. Neural graphics primitives leverage neural networks to represent complex scenes efficiently, while bundle adjustment refines camera parameters and 3D points for high-precision reconstructions. The proposed system integrates these methodologies to enhance rendering performance, accuracy, and scalability, making it a robust solution for applications like photorealistic rendering, virtual reality, and 3D scene reconstruction.

Introduction

The advent of neural graphics primitives (NGP) has revolutionized the fields of rendering and 3D modeling by enabling compact and efficient scene representations. However, challenges remain in achieving high accuracy and consistency in large-scale or dynamic scenes. Bundle adjustment, a well-established optimization technique in computer vision, offers a solution by refining the geometric structure and camera parameters of 3D scenes.

BAA-NGP merges these paradigms to address limitations in neural graphics primitives by incorporating bundle adjustment techniques. This integration ensures improved precision in scene geometry and texture representation while accelerating rendering processes, thereby bridging the gap between efficiency and accuracy in neural rendering pipelines.

Existing System

1. Traditional Graphics Pipelines:
   • Use polygonal meshes and rasterization for rendering.
   • Require manual optimization and are computationally expensive for complex scenes.
2. Neural Graphics Primitives:
   • Utilize neural networks for compact scene representation.
   • Struggle with dynamic adjustments for camera parameters or inconsistencies in multi-view inputs.
3. Bundle Adjustment Techniques:
   • Effective in refining 3D reconstructions but computationally intensive.
   • Not directly optimized for neural representations.

Proposed System

The proposed BAA-NGP framework integrates the advantages of neural graphics primitives with bundle adjustment to create a system that is:

• Accurate: Enhances scene reconstruction by refining neural representations using multi-view geometric constraints.
• Efficient: Leverages accelerated neural rendering techniques to maintain high performance.
• Scalable: Adapts to both small-scale and large-scale dynamic scenes.

Key features include:

• Optimization of camera parameters and neural graphics simultaneously.
• Use of lightweight neural architectures to reduce computational overhead.
• Real-time rendering with photorealistic quality.

Methodology

1. Data Acquisition:
   • Collect multi-view images or video data of the target scene.
2. Neural Graphics Primitives Initialization:
   • Use neural networks to encode the scene into a compact representation.
3. Bundle Adjustment Integration:
   • Apply bundle adjustment to optimize camera poses and 3D points based on the neural representation.
4. Joint Optimization:
   • Simultaneously refine neural graphics primitives and geometric parameters for consistency and accuracy.
5. Rendering:
   • Generate photorealistic outputs using optimized neural graphics.

Technologies Used

1. Programming Language: Python or C++ for implementation.
2. Deep Learning Frameworks: TensorFlow, PyTorch.
3. Graphics Libraries: OpenGL, Vulkan for rendering support.
4. Optimization Libraries: Ceres Solver for bundle adjustment.
5. Hardware: NVIDIA GPUs for accelerated neural rendering.
6. Mathematical Tools: CUDA for parallel computation, and matrix solvers for geometric refinement.