Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era (1906.06543v3)

Abstract: 3D reconstruction is a longstanding ill-posed problem, which has been explored for decades by the computer vision, computer graphics, and machine learning communities. Since 2015, image-based 3D reconstruction using convolutional neural networks (CNN) has attracted increasing interest and demonstrated an impressive performance. Given this new era of rapid evolution, this article provides a comprehensive survey of the recent developments in this field. We focus on the works which use deep learning techniques to estimate the 3D shape of generic objects either from a single or multiple RGB images. We organize the literature based on the shape representations, the network architectures, and the training mechanisms they use. While this survey is intended for methods which reconstruct generic objects, we also review some of the recent works which focus on specific object classes such as human body shapes and faces. We provide an analysis and comparison of the performance of some key papers, summarize some of the open problems in this field, and discuss promising directions for future research.

• The paper presents the first dedicated survey of 149 deep learning methods for image-based 3D reconstruction since 2015.
• It categorizes techniques by shape representations and network architectures to compare volumetric, surface, and point-based approaches.
• It examines diverse training strategies, including supervised, semi-supervised, and adversarial methods, and highlights future research directions.

Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era

The paper "Image-based 3D Object Reconstruction: State-of-the-Art and Trends in the Deep Learning Era" provides a comprehensive review of recent advancements in 3D reconstruction using deep learning techniques. This survey spans a wide array of methodologies developed since 2015, focusing on reconstructing generic objects from single or multiple RGB images through convolutional neural networks (CNNs) and other deep learning architectures.

Key Contributions and Overview

The paper makes several significant contributions:

1. It presents the first survey dedicated exclusively to deep learning-based image-based 3D object reconstruction, covering 149 methods published since 2015.
2. The survey provides a detailed review of methods, organizing them based on shape representation, network architectures, training procedures, and evaluation metrics.
3. It offers insights into the performance of key methods, summarizing their properties and comparative results.

The core focus is on understanding how different approaches leverage deep learning for 3D shape estimation, including volumetric, surface-based, and point-based representations. The paper also assesses how these methods handle input data, such as single vs. multi-image inputs and the use of additional cues like depth, silhouettes, and semantic labels.

Shape Representations and Network Architectures

The methods surveyed are categorized based on their shape representations: volumetric, surface-based, and point-based approaches. Volumetric methods often employ voxel grids, offering the advantage of adapting 2D convolutional techniques to 3D but suffering from high memory requirements. Surface-based methods utilize mesh or parameterizations to reconstruct surfaces with potentially higher fidelity but are limited by the complexity of irregular structures. Point-based approaches have gained traction due to their memory efficiency, although they require post-processing to derive final mesh outputs.

Network architectures play a crucial role in these reconstructions. The architectures typically include encoder-decoder configurations with variations like generative adversarial networks (GANs) for adversarial training, and recurrent neural networks (RNNs) for temporal correlation exploitation. These networks are trained to optimize reconstruction loss functions, which vary between volumetric and silhouette-based consistency to achieve high accuracy.

Training Techniques and Supervision

A critical aspect of deep learning models for 3D reconstruction is their training methodology. The survey highlights the use of supervised training with full 3D ground truth, semi-supervised, or even unsupervised strategies that rely on 2D cues like silhouettes and depth maps. Furthermore, some methods incorporate generative adversarial training to enhance generative capabilities in shape and structure.

Several methods introduce training innovations, such as TL-embedding to ensure joint 2D-3D latent space learning and integration of multiple reconnaissance tasks into a shared network to enhance both 3D reconstruction and object segmentation.

Evaluation and Future Directions

Evaluating 3D reconstruction methods involves various metrics, with intersection over union (IoU) and Chamfer distance (CD) being two commonly used indicators of spatial reconstruction accuracy. Benchmark datasets such as ShapeNet and Pix3D are frequently employed to compare models, although challenges remain regarding generalization to unseen object categories and scaling to higher-resolution outputs.

The paper speculates on future research directions, encouraging developments focusing on reducing supervision dependency, improving generalization, overcoming fine-scale reconstruction challenges, and integrating object recognition and scene understanding with 3D modeling capabilities. The emphasis is on developing models that can handle complex, cluttered scenarios and evolve with more extensive datasets and refined neural architectures.

In conclusion, the survey provides a broad picture of the advancements in image-based 3D reconstruction and outlines practical implications and future avenues for research. As this area continues to develop rapidly, ongoing efforts in diverse applications and theoretical explorations promise further breakthroughs in 3D vision technologies.