High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference (1709.07599v1)

Abstract: We propose a data-driven method for recovering miss-ing parts of 3D shapes. Our method is based on a new deep learning architecture consisting of two sub-networks: a global structure inference network and a local geometry refinement network. The global structure inference network incorporates a long short-term memorized context fusion module (LSTM-CF) that infers the global structure of the shape based on multi-view depth information provided as part of the input. It also includes a 3D fully convolutional (3DFCN) module that further enriches the global structure representation according to volumetric information in the input. Under the guidance of the global structure network, the local geometry refinement network takes as input lo-cal 3D patches around missing regions, and progressively produces a high-resolution, complete surface through a volumetric encoder-decoder architecture. Our method jointly trains the global structure inference and local geometry refinement networks in an end-to-end manner. We perform qualitative and quantitative evaluations on six object categories, demonstrating that our method outperforms existing state-of-the-art work on shape completion.

• The paper presents a two-stage framework that integrates global structure inference using 3D-FCN and LSTM-CF with local geometry refinement via a patch-based encoder-decoder.
• It effectively recovers missing regions in 3D shapes, outperforming state-of-the-art methods on completeness and normalized distance metrics across six object categories.
• The approach offers practical benefits for fields like VR, AR, robotics, and CAD by enabling precise, high-resolution reconstructions from incomplete data.

High-Resolution Shape Completion Using Deep Neural Networks for Global Structure and Local Geometry Inference

The paper presents an advanced approach for 3D shape completion, leveraging deep learning techniques to address the challenge of reconstructing high-resolution shapes from incomplete data, which is a common issue due to occlusions or limited viewpoints during data acquisition. The proposed method is particularly relevant to fields like computer vision and graphics, which often require accurate and complete 3D models for further processing and analysis.

Core Contributions

The research introduces a two-stage deep learning framework designed to predict and recover missing regions in 3D shapes. The framework consists of two jointly-trained sub-networks: a global structure inference network and a local geometry refinement network. These networks are orchestrated to provide both broad structural predictions and fine-grained local corrections.

1. Global Structure Inference: This segment of the architecture is responsible for deducing a holistic understanding of the shape from partial input data. It incorporates a 3D fully convolutional network (3D-FCN) alongside a long short-term memorized context fusion module (LSTM-CF) to process both volumetric data and multi-view depth information. This dual-input system is aimed at capturing the global structure effectively, providing contextual guidance for subsequent refinement processes.
2. Local Geometry Refinement: Focused on enhancing the details, this component employs a patch-based, volumetric encoder-decoder network. It specializes in progressive fine-tuning of high-resolution details within local patches, guided by the global predictions made by the first sub-network.

Methodology and Evaluation

The methodology reflects a thoughtful integration of global and local inference strategies. The global structure inference maps the incomplete shape to a low-resolution complete representation, serving as an essential contextual scaffold for further refinement. The local network then processes surface detail within high-resolution patches, benefiting from the global shape priors.

The paper evaluates its method across six object categories, demonstrating superior performance compared to existing state-of-the-art techniques for shape completion. The method achieves this by outperforming on metrics such as completeness and normalized distance, particularly in reconstructing fine-grained details that previous approaches—often limited by their coarse voxel grid representation—could not manage effectively.

Experimental Setup

Data preparation and experimental validation reflect a robust approach. Training data was generated by simulating standard acquisition scenarios with consumer-level depth cameras, accounting for common issues like occlusion. The training of networks involved a two-phase approach: pretraining the global network and then engaging in joint training with the local network, which highlights the interdependency and reinforcement between global and local inferences.

Implications and Future Directions

The implications of this research are significant for practical applications requiring detailed 3D reconstructions. The advancement towards high-resolution completion facilitates its integration into applications such as virtual reality, augmented reality, robotics, and computer-aided design, where precise 3D modeling is crucial.

For future research, extending this approach to more generalized datasets and exploring further optimization of high-resolution processing would be valuable. Additionally, integrating this method with dynamically acquired data in real-time systems could broaden its applicability and improve efficiency in scenarios demanding on-the-fly shape completion.

Overall, this paper marks a substantial step forward in the automated recovery of 3D shapes, bringing refinement to both its methodology and results in the domain of deep learning-based shape analysis.