Types of Animal Skeleton 3D Models

Animal skeleton 3d models

The animal skeleton is a framework of bones in respect to the host body. A 3D model is a mathematical representation of something that is used to portray the real-world and conceptual visuals for art, entertainment, drafting, and simulation.

Zooarchaeology is a discipline of archaeology that deals with knowledge of human occupation and their surroundings through the study of animal remains. With an increase in animals' shapes and remains, it is a challenge to develop expertise. Three-dimensional models, however, present a collective way to distinguish between different patterns among species.

Application of three-dimensional models builds proficiency in bone identification. You can create 3D by taking multiple photos from different angles and views. These images can then be transformed into virtual 3D models with the help of applications. Some of the applications help individuals view the images on their smartphones, tablets or computers.

It is usually cumbersome to use animal bone manuals which contain hand-drawn images from few static views. With the rise of 3D models, bone identification becomes easier by letting you interact with realistic images.

Although 3D is not like direct virtual, reconstructions could rival traditional reference books which are bulky and too expensive. With 3D, you can carry, transport, and manipulate by the swipe or click of a finger. 

Animal skeleton 3d models

Types of animal skeleton 3D models:

3D modeling decomposition is a challenge in computer graphics. Several models have been put across, but due to lack of enough evaluation and comparison, it is almost an impossible task.

Based on the current anatomy knowledge, animals are compared using alternative algorithms. The major problem you are likely to face is comparing mesh and volume segmentation based on geometric or semantic approaches.

  1. Mesh segmentation

Mesh segmentation has various applications such as texture mapping, animation, simplification, and compression. There are two different styles in mesh segmentation, the geometric and semantic.

  • Geometric segmentation

The mesh is segmented into several meaningless patches based on surface things such as distance on a suitable plane.

  • Semantic segmentation

The mesh is segmented into meaningful visual parts that are related to relevant features of the shape. For instance, you can segment a body part of a lion into head, body, legs, and tail. 

The semantic approach has advantages in relation to the application of animation, shape retrieval, skeleton extraction, and classification. The semantic approach is however problematic due to lack of consistency evaluation criteria to compare different techniques since each system has been developed to fit a different application.

The best way to compare segmentation is based on respective application. One may also find problems in the definition of different patches in 3D model, meaning quantity description may be challenging.

  1. Systematic segmentation

Systematic segmentation of quadrupeds is highly objective for object animation and retrieval. However, due to the problems, there is no metric to evaluate and compare segmentation results. So the general idea is to use the skeleton of the 3D model to guide decomposition. 

The framework of a 3D model is a compact graphical-like abstraction derived from the centerline of the original model. The next step you take is mapping superficial points into skeleton branches.

Animal skeleton 3d models

The surface points that match on skeleton branch are known as a patch. One can use 3D animal models and check the comparisons with animal anatomy to evaluate algorithms.

Semantically oriented technique tries to create segmentation patches such as cost function which is minimized based on certain criterion. Main alterations of algorithms in this section are the cost function.

The model is based on the mesh of a particular part, and the base is associated with input object. The nodes of higher level correspond to coarser patches while side nodes to finer patches. The algorithms determine the number of patches at each level and compute its segmentation.

The main benefit of this method is that it's insensitive to pose and proportions. The approach transforms the mesh vertices in a vibrant pose, then robustly extracts the feature points and finally sources the core component of the mesh.

You can propose an algorithm based on fittings like planes spheres and cylinders. Each triangle of a triangular mesh corresponds to a single cluster. All adjacent are considered and compared at each iteration. The best-approximated forms are the new cluster.

The shape diameter function   

This is another 3D approach, defined as a diameter of the object in the neighborhood of each point on the surface. Given a point on the surface mesh, a set of rays is sent inside a cone centered around its inward-normal direction to the other mesh. The value of SDF is the weighted average of the length of rays that fall within one standard deviation from the medium of all its length.

The idea is to generate a random set of segmentation and measure how often each edge of mesh lies on the boundary at a randomized set.

Segmentation based on skeletonization

Due to visual sensitivity, it is good to use structural simplification and segmentation processes. Since the projected 2D contour of 3D model vary significantly when the view is changed, it is more effective to use skeleton model during segmentation.

Decomposition step

  • Decompose the skeleton points. The points are those that connect to more than one branch.
  • Map surface nodes to skeleton points. This is based on the distance between l2 points on the skeleton vertices and surface nodes.
  • The final step is to label the neighboring nodes. The watershed is used to flood the labels from samples.


The above approaches are some of the different ways animal skeleton 3D models can be applied. They are based on professional expert knowledge. Some modification that can be used is to let the user have more courses of segmentation. Then one can generate the skeleton and segmentation according to the user requirements.