Learning Shape Priors for Single-View 3D Completion and Reconstruction (1809.05068v1)

Abstract: The problem of single-view 3D shape completion or reconstruction is challenging, because among the many possible shapes that explain an observation, most are implausible and do not correspond to natural objects. Recent research in the field has tackled this problem by exploiting the expressiveness of deep convolutional networks. In fact, there is another level of ambiguity that is often overlooked: among plausible shapes, there are still multiple shapes that fit the 2D image equally well; i.e., the ground truth shape is non-deterministic given a single-view input. Existing fully supervised approaches fail to address this issue, and often produce blurry mean shapes with smooth surfaces but no fine details. In this paper, we propose ShapeHD, pushing the limit of single-view shape completion and reconstruction by integrating deep generative models with adversarially learned shape priors. The learned priors serve as a regularizer, penalizing the model only if its output is unrealistic, not if it deviates from the ground truth. Our design thus overcomes both levels of ambiguity aforementioned. Experiments demonstrate that ShapeHD outperforms state of the art by a large margin in both shape completion and shape reconstruction on multiple real datasets.

Overview of "Learning Shape Priors for Single-View 3D Completion and Reconstruction"

The research paper titled "Learning Shape Priors for Single-View 3D Completion and Reconstruction" addresses the formidable challenge of generating complete and detailed 3D models from single depth or RGB images. This task is inherently complex due to the ambiguities associated with inferring 3D structures from limited 2D views. The authors propose a novel framework named ShapeHD, which combines deep generative models with adversarially learned shape priors, setting a new benchmark in the field.

Key Contributions and Methodology

The authors identify two primary issues in single-view 3D completion and reconstruction: the multiplicity of plausible shapes that can fit a 2D observation and the generation of unrealistic mean shapes by conventional deep learning models. They address these challenges by incorporating adversarially learned shape priors that act as a regularizer—penalizing the model for producing implausible outputs. This approach does not necessitate strict adherence to a singular ground truth, thus allowing the network to capture a broader range of valid 3D shapes.

ShapeHD includes three main components:

1. 2.5D Sketch Estimator: This module generates depth, surface normals, and silhouettes from RGB inputs using a ResNet-18-based encoder-decoder architecture.
2. 3D Shape Completion Network: It predicts 3D shapes using the 2.5D sketches as input. The network leverages volumetric convolutions to produce detailed 3D reconstructions.
3. Shape Naturalness Network: Utilizing generative adversarial training, this network evaluates the plausibility of shapes, offering a "naturalness loss" that guides the shape prediction network away from unrealistic mean shapes.

Experimental Evaluation

ShapeHD demonstrates superior performance across various datasets, including synthetic datasets like ShapeNet and real-world datasets like PASCAL 3D+ and Pix3D. Experimental results reveal substantial improvements in Intersection over Union (IoU) and Chamfer Distance (CD) metrics when compared to state-of-the-art models like 3D-EPN and 3D-R2N2.

Notably, ShapeHD generates reconstructions with greater detail and variety than previous models. The experimental analysis demonstrates the model's capacity to produce realistic and perceptually preferred 3D shapes, addressing the inherent ambiguity of single-view inputs effectively.

Implications and Future Directions

The integration of shape priors through adversarial learning in ShapeHD offers a promising path forward in handling the uncertainty and variability in single-view 3D reconstruction. This methodology not only enhances the quality of generated 3D models but also brings attention to the importance of leveraging learned priors in tackling ill-posed problems in computer vision.

Future research could explore the extension of this framework to broader categories of objects and more complex environments. Additionally, investigating the scalability and real-time applicability of ShapeHD could pave the way for its implementation in virtual reality, autonomous systems, and augmented reality applications.

ShapeHD exemplifies a significant advancement in 3D vision research, highlighting the efficacy of adversarial learning in refining the inference capabilities of deep neural networks within the domain of shape completion and reconstruction.