Adaptive Joint Optimization for 3D Reconstruction With Differentiable Rendering.

Area of Science:

• Computer Vision
• 3D Reconstruction
• Computer Graphics

Background:

• 3D reconstruction from RGB-D sensors is prone to noise, causing geometric and textural artifacts like camera drift and mesh distortion.
• Existing methods often optimize geometry, texture, or camera pose independently, leading to complex systems and suboptimal results.

Purpose of the Study:

• To propose a novel, unified optimization framework for 3D reconstruction using differentiable rendering.
• To integrate the joint optimization of camera pose, geometry, and texture.
• To enhance the photorealism and accuracy of reconstructed 3D models.

Main Methods:

• Developed a unified framework based on differentiable rendering to enforce consistency between rendered outputs and RGB-D inputs.
• Introduced a joint optimization approach leveraging inter-component relationships and an adaptive interleaving strategy for stability and efficiency.
• Incorporated an image-level adversarial loss for improved photorealism.

Main Results:

• Demonstrated superior performance in recovering fine-scale geometry and high-fidelity textures compared to previous methods.
• Achieved significant reductions in artifacts such as camera drifting, mesh distortion, and texture ghosting.
• Validated the approach through quantitative and qualitative evaluations on both synthetic and real-world datasets.

Conclusions:

• The proposed differentiable rendering-based unified framework offers a more effective and stable approach to 3D reconstruction.
• Joint optimization of pose, geometry, and texture significantly enhances the quality and photorealism of 3D models.
• This method advances the state-of-the-art in handling noise and artifacts in RGB-D based 3D reconstruction.