GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction (2206.14735v2)

Abstract: We present GO-Surf, a direct feature grid optimization method for accurate and fast surface reconstruction from RGB-D sequences. We model the underlying scene with a learned hierarchical feature voxel grid that encapsulates multi-level geometric and appearance local information. Feature vectors are directly optimized such that after being tri-linearly interpolated, decoded by two shallow MLPs into signed distance and radiance values, and rendered via surface volume rendering, the discrepancy between synthesized and observed RGB/depth values is minimized. Our supervision signals -- RGB, depth and approximate SDF -- can be obtained directly from input images without any need for fusion or post-processing. We formulate a novel SDF gradient regularization term that encourages surface smoothness and hole filling while maintaining high frequency details. GO-Surf can optimize sequences of $1$-$2$K frames in $15$-$45$ minutes, a speedup of $\times60$ over NeuralRGB-D, the most related approach based on an MLP representation, while maintaining on par performance on standard benchmarks. Project page: https://jingwenwang95.github.io/go_surf/

• The paper introduces a novel neural feature grid optimization technique that accelerates surface reconstruction by 60x compared to traditional MLP-based methods.
• It combines multi-resolution feature grids with a lightweight MLP decoder to capture fine details and global structure for high-fidelity RGB-D scene modeling.
• Extensive evaluations on synthetic and real-world datasets demonstrate robust performance in filling holes and recovering complex topologies, promising real-time applicability.

Overview of GO-Surf: Neural Feature Grid Optimization for Fast, High-Fidelity RGB-D Surface Reconstruction

The paper presents GO-Surf, a novel method for effective RGB-D surface reconstruction, leveraging neural feature grid optimization to achieve expedient, high-fidelity 3D scene modeling. Distinguishing itself from traditional multi-layer perceptron (MLP) based models, GO-Surf focuses on direct optimization of a multi-resolution feature grid, enabling the system to operate at a speed sixty times faster than comparable MLP-based methodologies without reducing reconstruction performance. Such gains in efficiency position GO-Surf as a promising tool for applications where rapid surface inference is critical, including robotics and augmented reality scenarios.

Methodology

The authors introduce an innovative hybrid scene representation that combines multi-level feature grids with lightweight MLP decoders to predict geometric and appearance features of a scene. The employed grid models encode hierarchical local details, capturing both high-frequency intricacies and broader structural information, which proves beneficial in reconstructing surfaces with accuracy and completeness.

The method implements a novel SDF gradient regularization term that promotes smooth surface reconstructions and efficient hole filling, preserving critical detailed features. GO-Surf directly optimizes feature vectors through a concise, yet effective, rendering pipeline using a two-stage process:

1. Depth and Color Rendering: By sampling numerous points along camera rays, the rendered images integrate RGB and depth values to minimize the error between synthesized and observed imagery.
2. Approximate SDF Supervision: Inspired by the necessity to maintain algorithmic robustness, an approximate SDF supervision model closely monitors the distance between predicted and true surface locations, essential for accurately reconstructing high-fidelity surfaces.

The architecture is uniquely designed to enhance performance: feature grids of increasing resolution are iteratively optimized, allowing for accelerated convergence without the heavy computational burden typical of other neural network approaches.

Experimental Evaluation

The approach is validated through extensive evaluations on both synthetic and real-world datasets, such as ScanNet. Qualitatively, GO-Surf demonstrates superior ability in handling scenes with missing depth information and challenging topologies, thanks to its capacity for hole filling and maintaining smooth, natural surfaces. Quantitatively, it performs competitively with state-of-the-art systems like NeuralRGB-D, achieving similar accuracy, completion, and normal consistency metrics, and excelling particularly in speed, completing in 15 to 45 minutes compared to the 15 to 25 hours required by others.

Additionally, ablation studies underscore the importance of RGB loss for detailed reconstruction and the smoothness prior for avoiding overfitting to noise. The paper also acknowledges its systematic approach in refining camera pose estimates, showcasing incremental improvements in both translation and rotational accuracy.

Implications and Future Work

GO-Surf’s contribution is pivotal in advancing efficient 3D surface reconstruction methods by merging state-of-the-art neural network strategies with practical performance enhancements. Potential future work includes addressing the memory footprint challenge—stemming from complex scene dimensions—and further exploring model generalization to various environments, possibly through integration with sparse grid techniques such as voxel hashing and octree-based representations. Moreover, simplification of loss terms might offer a more intuitive understanding and robust training process, aligning with the broader goals of scalability and real-time application.

In conclusion, GO-Surf exemplifies a significant step towards melding high-speed operation with high accuracy in neural surface reconstruction technologies. Its implementation could shape future research, particularly in scenarios demanding interactive, real-time processing.