NeuralRoom: Geometry-Constrained Neural Implicit Surfaces for Indoor Scene Reconstruction

Abstract: We present a novel neural surface reconstruction method called NeuralRoom for reconstructing room-sized indoor scenes directly from a set of 2D images. Recently, implicit neural representations have become a promising way to reconstruct surfaces from multiview images due to their high-quality results and simplicity. However, implicit neural representations usually cannot reconstruct indoor scenes well because they suffer severe shape-radiance ambiguity. We assume that the indoor scene consists of texture-rich and flat texture-less regions. In texture-rich regions, the multiview stereo can obtain accurate results. In the flat area, normal estimation networks usually obtain a good normal estimation. Based on the above observations, we reduce the possible spatial variation range of implicit neural surfaces by reliable geometric priors to alleviate shape-radiance ambiguity. Specifically, we use multiview stereo results to limit the NeuralRoom optimization space and then use reliable geometric priors to guide NeuralRoom training. Then the NeuralRoom would produce a neural scene representation that can render an image consistent with the input training images. In addition, we propose a smoothing method called perturbation-residual restrictions to improve the accuracy and completeness of the flat region, which assumes that the sampling points in a local surface should have the same normal and similar distance to the observation center. Experiments on the ScanNet dataset show that our method can reconstruct the texture-less area of indoor scenes while maintaining the accuracy of detail. We also apply NeuralRoom to more advanced multiview reconstruction algorithms and significantly improve their reconstruction quality.

• The paper introduces a geometry-constrained neural implicit representation to mitigate shape-radiance ambiguity in indoor scene reconstruction.
• It integrates depth and normal priors from MVS and learning networks, enhancing the fidelity and structural integrity of texture-less regions.
• Experimental results on ScanNet reveal improved F-ratios and accuracy, underscoring its potential for VR/AR and autonomous mapping applications.

NeuralRoom: Geometry-Constrained Neural Implicit Surfaces for Indoor Scene Reconstruction

NeuralRoom proposes a novel approach in reconstructing indoor environments by leveraging geometry-constrained neural implicit surfaces derived from images. This paper addresses indoor scene reconstruction's inherent challenges, such as shape-radiance ambiguity, which often hampers the efficacy of current neural rendering methods when applied to texture-less environments typical of indoor settings.

Method Overview

Geometry-Constrained Neural Implicit Surfaces

NeuralRoom utilizes implicit neural representations to overcome the shape-radiance ambiguity in indoor reconstructions. The method begins with acquiring geometry priors, which consist of depth and normal estimations derived from multiview stereo methods (MVS) and learning-based normal estimation networks, respectively.

Figure 1: Different areas in which priors take effect.

NeuralRoom involves rendering from 2D images with known camera parameters, guided by geometric priors to ensure that the reconstructed scene coheres with the original images in terms of texture and structural integrity.

Training NeuralRoom with Perturbation-Residual Restrictions

To enhance the continuity and smoothness of the reconstructed surfaces, the paper introduces perturbation-residual restrictions. These constraints assume a local uniformity in normal direction and minor variation in distance across neighboring surface segments. This approach helps maintain continuity and prevent discrepancies commonly induced by similar texture-less surfaces with distinct physical separations.

Figure 2: Two cases illustrating perturbation-residual restriction.

Image Processing and Mesh Extraction

Starts with preprocessing image sequences, capturing optimal images for stereo matching using the Laplacian filter for blurriness detection. After constructing the distance and normal priors, NeuralRoom employs a differentiable renderer integrated with these geometry cues, subsequently extracting mesh via marching cubes and refining through TSDF fusion.

Experimental Evaluation

NeuralRoom demonstrates significantly improved reconstruction quality compared to traditional methods such as COLMAP and learning-based methods like Atlas and NeuralRecon. This enhancement is primarily attributed to its adept handling of texture-less areas using reliable geometric priors.

Figure 3: Visualization results on ScanNet.

Quantitatively, NeuralRoom showcases superior F-ratios and accuracy across several test scenes in the ScanNet dataset, indicating a balanced approach in terms of both completeness and detail fidelity.

Implications and Limitations

Practical Applications

The implications of NeuralRoom are profound, with potential applications spanning across VR/AR, indoor mapping for autonomous systems, and enhanced spatial fidelity for CAD models. The ability to reconstruct without relying on depth sensors further facilitates its application in environments with reflective or absorptive surfaces.

Figure 4: Advantages and limitations.

Computational Considerations

However, these benefits come at the expense of computational resources and setup reliance. The algorithm's dependency on accurate pose and prior estimates still poses constraints. Future advancements could mitigate these with more sophisticated learning-based estimation modules and efficient renderer architectures, thereby expediting and simplifying deployment processes.

Conclusion

NeuralRoom advances the field of neural surface reconstruction by integrating geometry priors into rendering processes, thereby overcoming traditional model limitations posed by texture-less indoor regions. By fostering more accurate and visually coherent reconstructions, NeuralRoom opens avenues for enhanced spatial representation and fidelity in various computer vision and graphics applications.