How accurate are 3D facial models created from CT scans compared to traditional photography?5 answersThe accuracy of 3D facial models created from CT scans compared to traditional photography varies based on the method used. Research by Alvarez-Cascos et al. demonstrated that 3D facial scanners provide acceptable accuracy values, outperforming 2D photography in facial analysis precision. Mao et al. found that a 3D face reconstruction method based on 2D photos yielded clinically acceptable results for orthodontic measurements, despite labor-intensive operations. Chaves et al. evaluated 3D facial soft tissue models from photogrammetry software and noted reasonably accurate results compared to CBCT scans, with discrepancies ranging from 2.11mm to 5.17mm. Additionally, Wang's study introduced a method using AI for 3D head anatomy reconstruction from facial photos, achieving a facial surface reconstruction error of 3.60±0.49 mm, surpassing existing CNN-based methods.
Hasan Kemal Surmen, “Photogrammetry for 3D Reconstruction of Objects: Effects of Geometry, Texture and Photographing”, (2023)5 answersPhotogrammetry is a valuable method for 3D reconstruction due to its portability, cost-effectiveness, and ability to capture various object textures and geometries. Factors like object texture and range influence the accuracy of reconstructions, with insufficient texture leading to increased noise levels and potential errors. Software choice is crucial, with Agisoft outperforming Meshroom and MeshLab in terms of optimization techniques, processing time, visual quality, and size measurement accuracy. Comparisons between scanning devices like 360 cameras and DSLRs show their varying properties in 3D reconstruction, highlighting their applications in fields like virtual reality and 3D printing. Overall, understanding the impact of geometry, texture, software, and hardware choices is essential for optimizing the photogrammetric 3D reconstruction process.
What are the biases and accuracy of facial recognition algorithms?4 answersFacial recognition algorithms can exhibit biases and variations in accuracy. Studies have shown that algorithms can outperform humans in certain cases, but both humans and machines show race-based accuracy differences. The racial distribution of faces in the training data can affect the performance of face recognition models, and a uniform distribution alone does not guarantee bias-free algorithms. Pre-trained models can learn implicit biases, including racial biases, and these biases can impact fairness in society. Factors such as image quality, population statistics, algorithm architecture, and threshold decisions can contribute to race bias in algorithms. Dataset difficulty and identification thresholds can also influence the degree of bias in recognition accuracy. It is important to measure and assess race bias in face recognition algorithms for individual applications.
What is photogrammetry?3 answersPhotogrammetry is a technique used to obtain a reliable database of any physical object by creating a digital 3D model using multiple photos taken at different angles around the object. It involves processing a set of images to compute the spatial reconstruction of a scene or object, allowing for measurements or reconstructions of the observed object. Photogrammetry has been used in various fields such as topography, architecture, engineering, medicine, and dentistry. It has also played a significant role in government-supported geomedia infrastructure and spatial information capture. The advantages of photogrammetry include the ability to record the shape of an object quickly and accurately without contact, as well as the generation of models that can be displayed without textures. Overall, photogrammetry is a method that utilizes photographs to derive three-dimensional data about objects or terrain from two-dimensional imaging.
How do neural radiance fields compare to photogrammetry in terms of accuracy, efficiency, and flexibility?5 answersNeural radiance fields (NeRFs) offer significant potential for speedups and easier learning compared to photogrammetry in terms of accuracy, efficiency, and flexibility. NeRFs learn a mapping from position to volumetric density, which involves no view-dependent effects and is thus easier to learn. They can store valuable density information in their parameters or deep features, reducing computational requirements by up to 50% with only a slight hit in rendering quality. NeRFs also excel in capturing and reproducing specular details of surfaces, even without positional encoding. In contrast, photogrammetry requires lengthy training times and often fails to accurately capture specular details. Additionally, NeRFs enable shadow detection, albedo synthesis, and transient object filtering without explicit shape supervision.
Was trust propensity ever measured with faces as stimuli?5 answersTrust propensity has been measured with faces as stimuli in several studies. Zhao et al. found that individuals' implicit responses were influenced by the level of trust when encountering attractive or unattractive faces. Feng et al. used a prediction framework to examine the relationship between trust propensity and brain measures, revealing that gray matter volume and node strength in specific regions predicted trust propensity. Tsankova et al. investigated the interplay of dynamic information from the face and voice and found that facial cues had a primacy effect on trustworthiness perception. Tingley studied a trust game where participants chose avatars representing them, and found that individuals were more likely to choose avatars that were perceived as more trustworthy, and these avatars influenced strategy choices and trust behavior. Janssens et al. used transcranial magnetic stimulation to investigate the neural pathways involved in trustworthiness perception and found that conscious perception of trustworthiness relied on intact early visual cortex.