Traditionally, single-view object reconstruction models built on convolutional neural networks have demonstrated outstanding performance on reconstruction tasks.
In recent years, single-view 3D reconstruction has emerged as a popular research topic in the artificial intelligence community.
Regardless of the specific methodology used, all single-view 3D reconstruction models share the common approach of incorporating an encoder-decoder network into their framework.
This network performs complex reasoning about the 3D structure in the output domain.
In this article, we will examine how single-view 3D reconstruction works in real time and the current challenges these frameworks face in reconstruction tasks.
We will discuss the various key components and methods used by single-view 3D reconstruction models and explore strategies that can improve the performance of these frameworks.
We will also analyze the results produced by state-of-the-art frameworks using encoder-decoder methods. Let’s dive.
Single View 3D Object Reconstruction
Single view 3D object reconstruction involves creating a 3D model of an object from a single viewpoint or, more simply, from a single image.
For example, extracting the 3D structure of an object, such as a motorcycle, from an image is a complex process.
It combines information about the structural arrangement of parts, low-level image cues, and high-level semantic information.
This spectrum covers two main elements: reconstruction and recognition . The reconstruction process distinguishes the 3D structure of the input image using cues such as shading, texture, and visual effects.
In turn, the recognition process classifies the input image and retrieves a suitable 3D model from the database.
Existing single-view 3D object reconstruction models may differ architecturally but are unified by the inclusion of an encoder-decoder structure in their framework.
In this structure, the encoder maps the input image to a latent representation, while the decoder makes complex inferences about the 3D structure of the output field. To successfully carry out this task, the network must integrate both high-level and low-level information.
Additionally, many state-of-the-art encoder-decoder methods rely on recognition for single-view 3D reconstruction tasks, which limits their reconstruction capabilities.
Moreover, the performance of modern convolutional neural networks in single-view 3D object reconstruction can be surpassed without explicitly inferring the 3D object structure.
However, the dominance of recognition in convolutional networks in single-view object reconstruction tasks is affected by various experimental procedures, including evaluation protocols and dataset composition.
Such factors enable the framework to find a shortcut, in this case image recognition.
Traditionally, Single View 3D object reconstruction frameworks approach reconstruction tasks using texture and shape from the defocus and shading approach, which serve as exotic views for reconstruction tasks.
Because these techniques use a single depth cue, they are capable of inferring visible parts of a surface.
Moreover, many single-view 3D reconstruction frameworks use multiple cues as well as structural information to estimate depth from a single monocular image; a combination that allows these frames to estimate the depth of visible surfaces.
Newer depth estimation frameworks deploy convolutional neural network structures to reveal depth in a monocular image.
However, for effective single-view 3D reconstruction, models not only need to reason about the 3D structure of visible objects in the image but also need to hallucinate unseen parts in the image using certain prior knowledge learned from the data.
To achieve this, most models use already trained convolutional neural network structures to map 3D images to 3D shapes using direct 2D control; many other frameworks use voxel-based representations of the 3D shape and use a latent representation.
Create 3D upconvolutions. Certain frameworks also split the output space hierarchically to increase computational and memory efficiency, which allows the model to predict higher-resolution 3D shapes.
Recent research focuses on using weaker forms of supervision for single-view 3D shape predictions using convolutional neural networks;
it either compares predicted shapes and their ground truth predictions to train shape regressors or uses multiple learning signals to train average shapes that help the model predict.
deformations. Another reason behind the limited advances in single-view 3D reconstruction is the limited amount of training data available for the task.
Single-view 3D reconstruction is a complex task as it interprets visual data not only geometrically but also semantically.
Although not completely different, they cover different spectrums, from geometric reconstruction to semantic recognition.
Tasks of per-pixel reconstruction of the 3D structure of the object in the image. Reconstruction tasks do not require a semantic understanding of the content of the image and can be accomplished using low-level image cues such as texture, color, shading, shadows, perspective, and focus.
Recognition, on the other hand, is an extreme example of using image semantics because recognition tasks use entire objects and quantities to classify the object in the input and retrieve the corresponding shape from the database.
Although recognition tasks enable sound reasoning about parts of the object that are not visible in images, semantic resolution is only possible when it can be described by an object present in the database.
Although recognition and reconstruction tasks differ significantly from each other, both tend to ignore valuable information contained in the input image.
To achieve the best possible results, it is recommended to use both of these tasks in concert with each other and use accurate 3D shapes for object reconstruction; that is, for optimal single-view 3D reconstruction tasks, the model must use structural information, low-level image cues, and high-level understanding of the object.
Single View 3D Reconstruction: Conventional Setup
To explain the traditional setup and analyze the setup of single-view 3D reconstruction framework, we will implement a standard setup for estimating 3D shape using a single view or image of the object.
The dataset used for training purposes is the ShapeNet dataset and evaluates the performance on 13 classes, which allows the model to understand how the number of classes in a dataset determines the shape prediction performance of the model.
The majority of modern convolutional neural networks use a single image to estimate high-resolution 3D models, and these frameworks can be categorized according to the representation of their output: depth maps, point clouds, and voxel grids.
The model uses OGN or Octree Generating Networks as the representation method, which has historically outperformed the voxel grid approach and/or can cover dominant output representations.
Unlike existing methods that use output representations, the OGN approach allows the model to predict high-resolution shapes and uses octal trees to efficiently represent occupied space.
base
To evaluate the results, the model uses two baselines, treating the problem solely as a recognition task. The first baseline performs clustering, while the second baseline performs database retrieval.
clustering
The clustering basis model uses the K-Means algorithm to cluster or group the training shapes into K subcategories and runs the algorithm on 32*32*32 voxelizations flattened into a vector.
Once the cluster assignments are determined, the model returns to working with higher resolution models. The model then calculates the average shape within each cluster and maximizes the average IoU or Intersection Over Union over the models, thresholding the average shapes from which the best fit is calculated.
Since the model knows the relationship between 3D shapes and images in the training data, it can easily match the image with the corresponding cluster.
undo
The access baseline learns to place shapes and images in a common space. The model takes into account the pairwise similarity of 3D matrix shapes in the training set to generate the embedding space.
The model achieves this by using Multidimensional Scaling using the Sammon mapping approach to compress each row in the matrix into a low-dimensional descriptor.
Additionally, to calculate the similarity between two arbitrary shapes, the model uses the light field descriptor. Additionally, the model trains a convolutional neural network to map images to a descriptor to place images in space.
Analysis
Single-view 3D reconstruction models follow different strategies, outperforming other models in some areas but falling short in others.
We have different metrics to compare different frameworks and evaluate their performance; one of them is the average IoU score.
As can be seen in the image above, despite having different architectures, current state-of-the-art 3D reconstruction models provide almost similar performance. However, it is interesting to note that despite being a pure recognition method, the retrieval framework outperforms other models in terms of mean and median IoU scores.
The clustering framework AtlasNet delivers robust results, outperforming OGN and Matryoshka frameworks.
However, the most unexpected result of this analysis is that Oracle NN outperforms all other methods despite using a perfect access architecture.
Although calculating the average IoU score helps with comparison, it does not provide a complete picture as the variance in results is high regardless of model.
Common Evaluation Metrics
Single View 3D Reconstruction models often use different evaluation metrics to analyze their performance on a wide range of tasks. Below are some of the commonly used evaluation metrics.
Intersection Through Unity
Intercept Average Over Join is a metric commonly used as a quantitative measure to serve as a benchmark. single-view 3D reconstruction models .
Although IoU provides some insight into the performance of the model, it is not considered the sole metric for evaluating a method; because it only indicates the quality of the shape predicted by the model when the values are high enough and a significant discrepancy between the values is observed. low and mid range scores for the two given figures.
Chamfer Distance
Chamfer Distance is defined on point clouds and is designed to be satisfactorily applied to different 3D representations.
However, the Chamfer Distance evaluation metric is highly sensitive to outliers, making it a problematic metric to evaluate the model’s performance; The distance of the outlier from the reference shape significantly determines the production quality.
F Score
F-Score is a common evaluation metric actively used by the majority of multi-view 3D reconstruction models.
The F-Score metric is defined as the harmonic mean between recall and precision and explicitly evaluates the distance between objects’ surfaces.
Precision counts the percentage of reconstructed points that are within a predefined distance from the ground truth to measure the accuracy of the reconstruction.
Recall, on the other hand, counts the percentage of points on the ground truth that lie within a predefined distance from the reconstruction to measure the completeness of the reconstruction.
Additionally, developers can control the accuracy of the F-Score metric by changing the distance threshold.
Analysis per Class
The similarity in performance provided by the above frameworks cannot be a result of methods operating on different subsets of classes, and the figure below shows consistent relative performance between different classes, with the Oracle NN retrieval baseline achieving the best of any. Methods that observe high variance for all classes.
Additionally, the number of training examples available for a class may lead to assume that this affects performance per class.
However, as shown in the figure below, the number of training examples available for a class does not affect the performance per class, and there is no correlation between the number of examples in a class and the average IoU score.
Qualitative Analysis
The quantitative results discussed in the above section are supported by qualitative results as shown in the image below.
For the majority of classes, there is no significant difference between the clustering baseline and the predictions made by decoder-based methods.
The clustering approach fails to yield results when the distance between the sample and the average cluster shape is high or the average shape itself does not describe the cluster well enough.
On the other hand, frameworks that use decoder-based methods and access architecture give the most accurate and interesting results because they can include fine details in the created 3D model.
Single View 3D Reconstruction: Final Thoughts
In this article, we talked about Single View 3D Object Reconstruction, talked about how it works, and talked about two baselines: Retrieval and Classification, where the retrieval baseline approach outperforms current state-of-the-art models. Finally, however, Single View 3D Object Reconstruction is one of the hottest and most researched topics in the AI community, and although it has made significant progress in the last few years, Single View 3D Object Reconstruction is far from perfect, with significant hurdles to overcome in the coming years.
