GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement
(2406.05649)Abstract
We propose a novel approach for 3D mesh reconstruction from multi-view images. Our method takes inspiration from large reconstruction models like LRM that use a transformer-based triplane generator and a Neural Radiance Field (NeRF) model trained on multi-view images. However, in our method, we introduce several important modifications that allow us to significantly enhance 3D reconstruction quality. First of all, we examine the original LRM architecture and find several shortcomings. Subsequently, we introduce respective modifications to the LRM architecture, which lead to improved multi-view image representation and more computationally efficient training. Second, in order to improve geometry reconstruction and enable supervision at full image resolution, we extract meshes from the NeRF field in a differentiable manner and fine-tune the NeRF model through mesh rendering. These modifications allow us to achieve state-of-the-art performance on both 2D and 3D evaluation metrics, such as a PSNR of 28.67 on Google Scanned Objects (GSO) dataset. Despite these superior results, our feed-forward model still struggles to reconstruct complex textures, such as text and portraits on assets. To address this, we introduce a lightweight per-instance texture refinement procedure. This procedure fine-tunes the triplane representation and the NeRF color estimation model on the mesh surface using the input multi-view images in just 4 seconds. This refinement improves the PSNR to 29.79 and achieves faithful reconstruction of complex textures, such as text. Additionally, our approach enables various downstream applications, including text- or image-to-3D generation.
Overview
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The paper introduces advanced methodologies to improve 3D mesh reconstruction from multi-view images, focusing on enhancements in transformer-based models and Neural Radiance Field (NeRF) architectures.
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Key advancements include the replacement of the DINO transformer with convolutional encoders, the use of Pixelshuffle layers to reduce grid artifacts, and the separation of MLPs for density and color prediction.
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The proposed method shows significant performance improvements in various benchmarks and demonstrates potential applications in virtual reality and digital content creation through high-fidelity 3D reconstructions and efficient processing.
GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement
The paper "GTR: Improving Large 3D Reconstruction Models through Geometry and Texture Refinement" introduces an advanced methodology designed to enhance the quality and computational efficiency of 3D mesh reconstruction from multi-view images. The study builds upon transformer-based triplane generators and Neural Radiance Field (NeRF) models, critically analyzing the limitations within existing architectures, specifically the Large Reconstruction Models (LRM).
Key Contributions and Methodological Advancements
The framework proposed comprises several critical modifications aimed at addressing performance bottlenecks and boosting the fidelity of 3D reconstructions:
- Transformer and Convolutional Encoder Enhancements: The paper identifies limitations in using DINO features due to the loss of high-frequency image details. To tackle this, the researchers replace the DINO transformer with a convolutional encoder, enhancing the retention of essential image features. This leads to better representation and efficiency in training.
- Pixelshuffle Upsampling: Grid-shaped artifacts, as observed in the standard LRM pipeline, are mitigated by substituting deconvolution layers with Pixelshuffle layers. This adjustment helps reduce regular grid-like artifacts, thereby improving the visual quality of the reconstructions.
- Separation of MLPs for Density and Color: The architecture separates Multi-layer Perceptrons (MLPs) for predicting density and colors. This structural change not only facilitates better performance but also significantly aids in downstream fine-tuning processes, specifically in texture refinement stages.
- Differentiable Mesh Extraction and NeRF Fine-Tuning: For improving geometry reconstruction, the researchers employ Differentiable Marching Cubes (DiffMC) to transform the NeRF density fields into Signed Distance Functions (SDF). This transformation enables mesh extraction, which is then used for supervised training, guiding fine-tuning via mesh rendering.
- Per-Instance Texture Refinement: To address challenges in reconstructing complex textures, a lightweight per-instance texture refinement procedure is introduced. This involves fine-tuning the triplane representation and NeRF’s color estimation model specifically on the mesh surface using the multi-view images. This process is remarkably efficient, completing in just 4 seconds on an A100 GPU.
Performance and Evaluation
The proposed method demonstrates state-of-the-art performance across several benchmarks:
- Achieves a Peak Signal-to-Noise Ratio (PSNR) of 29.79 on the Google Scanned Objects (GSO) dataset, improving from 28.67 without texture refinement.
- Shows considerable improvements in 3D metrics such as Chamfer Distance (CD) and Intersection over Union (IoU), with significant performance boosts compared to concurrent methods.
- Delivers 18% improvement in PSNR, 30% in Learned Perceptual Image Patch Similarity (LPIPS), and 33% in CD, underscoring the effectiveness of the method.
Implications and Future Applications
From a practical perspective, the GTR method brings several advantages:
- High-Fidelity 3D Reconstructions: By addressing both geometry and texture fidelity through its hybrid approach, GTR enables highly accurate 3D asset generation usable in VR and digital content creation.
- Efficiency: The lightweight refinement techniques ensure rapid processing times, making the method suitable for real-time or near-real-time applications.
On the theoretical front, GTR sets a precedent for future research in hybrid approaches combining implicit and explicit geometry representations. The successful integration of differentiable mesh representation and NeRF fine-tuning could inspire further exploration into similar hybrid models, not just in vision-related tasks but possibly extending into other modalities in AI.
Future Directions
Looking forward, several avenues present themselves for building on this work:
- Incorporation of Pre-trained Models: Leveraging pre-trained encoders like those in Stable Diffusion models could potentially enhance convergence speeds and initial performance.
- Normal Loss and Geometry Smoothness: Implementing a normal loss could further improve surface smoothness in the generated meshes, addressing any residual artifacts.
- Exploration of Alternative Initializations: Investigating alternative initializations, such as those provided by NeuS for SDF fields, may yield higher fidelity starting points for mesh refinement.
By closely monitoring the trade-offs between computational efficiency and the fidelity of the reconstructions, future research can continue pushing the boundaries of what is achievable in 3D asset generation from multi-view images.
In sum, this paper makes significant strides in refining and enhancing 3D reconstruction techniques, demonstrating the value of a hybrid approach that balances computational efficiency with high fidelity in geometry and texture representation.