ScanComplete: Large-Scale Scene Completion and Semantic Segmentation for 3D Scans (1712.10215v2)

Abstract: We introduce ScanComplete, a novel data-driven approach for taking an incomplete 3D scan of a scene as input and predicting a complete 3D model along with per-voxel semantic labels. The key contribution of our method is its ability to handle large scenes with varying spatial extent, managing the cubic growth in data size as scene size increases. To this end, we devise a fully-convolutional generative 3D CNN model whose filter kernels are invariant to the overall scene size. The model can be trained on scene subvolumes but deployed on arbitrarily large scenes at test time. In addition, we propose a coarse-to-fine inference strategy in order to produce high-resolution output while also leveraging large input context sizes. In an extensive series of experiments, we carefully evaluate different model design choices, considering both deterministic and probabilistic models for completion and semantic inference. Our results show that we outperform other methods not only in the size of the environments handled and processing efficiency, but also with regard to completion quality and semantic segmentation performance by a significant margin.

• The paper introduces a fully-convolutional 3D CNN that transforms incomplete scans into complete, semantically labeled 3D models.
• It employs a coarse-to-fine strategy with a hierarchical architecture to efficiently handle large-scale scenes.
• Experimental results show notable improvements in reconstruction error and segmentation accuracy over traditional methods.

ScanComplete: Advancements in 3D Scene Completion and Semantic Segmentation

The paper "ScanComplete" introduces a significant advancement in the domain of 3D scene completion and semantic segmentation. The proposed method effectively bridges gaps in 3D reconstructions, which often suffer from deficiencies due to occlusions and the inherent limitations of range sensors. By leveraging a novel data-driven approach based on fully-convolutional neural networks, the authors address the challenge of transforming incomplete 3D scans into comprehensive, semantically labeled 3D models.

Key Contributions and Methodology

The core innovation of the paper is its ability to manage large-scale scenes with diverse spatial extents, ensuring the method remains invariant to scene size. This is achieved through a fully-convolutional 3D CNN model that handles cubic growth in data size—a common challenge in 3D data processing—by training on scene subvolumes and deploying on arbitrarily large scenes. The authors further refine the network’s output through a coarse-to-fine strategy, enhancing resolution while leveraging extensive input contexts.

Moreover, the research explores different model design configurations, balancing deterministic and probabilistic approaches for both completion and semantic tasks. The deterministic model showed superior performance by focusing on the single optimal solution for a scene’s physical reality, improving completion quality as opposed to a probabilistic approach, which is better suited for scenarios with multiple probable outputs.

Experimental Results and Comparison

The experimental evaluations demonstrate that the proposed methodology remarkably outperforms existing methods, such as Poisson Surface Reconstruction and SSCNet, in reconstruction error and semantic segmentation accuracy. The introduction of a hierarchical architecture, combined with an autoregressive model, enables efficient processing of extensive environments, achieving unprecedented completion and semantic inference accuracy. Specifically, employing a three-level hierarchy with a large spatial context significantly advances the prediction of global structural integrity and detailed local geometry.

Implications and Future Directions

Practically, the improvements in scene completion can directly enhance applications in virtual reality, content creation for graphics, and augmented reality, which rely on complete and accurate 3D maps of environments. Theoretically, the paper establishes a framework for future exploration into higher resolution outputs, potentially improving the method’s applicability to scenarios requiring finer detail, such as smaller object recognition within scenes. Moreover, there lie opportunities to explore end-to-end training methodologies across all hierarchical levels, which could further optimize performance and eliminate multi-step training dependencies.

The ability of the ScanComplete method to generalize and transfer learning from synthetic data to real-world scenarios is noteworthy, suggesting robust cross-domain applicability. However, additional research could further investigate the model's effectiveness across diverse real-world sensor data, refining adaptability and precision. This line of work could pioneer enhancements in automated scene understanding and environment reconstruction, crucial components in the advancing field of robotics and autonomous systems.

In conclusion, the ScanComplete framework presents a robust solution to longstanding issues in 3D scanning technology, providing pathways for both immediate applications and extensive future research into the intricacies of 3D data processing and semantic understanding.