by jsendak | Sep 6, 2024 | Computer Science
arXiv:2409.03336v1 Announce Type: cross
Abstract: Measuring 3D geometric structures of indoor scenes requires dedicated depth sensors, which are not always available. Echo-based depth estimation has recently been studied as a promising alternative solution. All previous studies have assumed the use of echoes in the audible range. However, one major problem is that audible echoes cannot be used in quiet spaces or other situations where producing audible sounds is prohibited. In this paper, we consider echo-based depth estimation using inaudible ultrasonic echoes. While ultrasonic waves provide high measurement accuracy in theory, the actual depth estimation accuracy when ultrasonic echoes are used has remained unclear, due to its disadvantage of being sensitive to noise and susceptible to attenuation. We first investigate the depth estimation accuracy when the frequency of the sound source is restricted to the high-frequency band, and found that the accuracy decreased when the frequency was limited to ultrasonic ranges. Based on this observation, we propose a novel deep learning method to improve the accuracy of ultrasonic echo-based depth estimation by using audible echoes as auxiliary data only during training. Experimental results with a public dataset demonstrate that our method improves the estimation accuracy.
Echo-Based Depth Estimation Using Inaudible Ultrasonic Echoes: A Multi-Disciplinary Approach
Echo-based depth estimation has gained attention in recent years as an alternative solution for measuring 3D geometric structures of indoor scenes in situations where dedicated depth sensors are not available. While previous studies on this topic have focused on echoes in the audible range, this research aims to explore the use of inaudible ultrasonic echoes. This approach opens up new possibilities for depth estimation in quiet spaces or environments where producing audible sounds is prohibited.
One key challenge faced by researchers is determining the accuracy of depth estimation when using ultrasonic echoes. Ultrasonic waves theoretically provide high measurement accuracy, but their effectiveness in practice has been unclear due to their sensitivity to noise and susceptibility to attenuation. To address this issue, the authors of this paper conducted a comprehensive investigation of depth estimation accuracy using restricted high-frequency ultrasonic waves.
The results of the investigation revealed that the accuracy of depth estimation decreased when the frequency was limited to the ultrasonic range. This finding highlights the need for innovative approaches to improve the performance of ultrasonic echo-based depth estimation. In response, the authors propose a novel deep learning method that leverages audible echoes as auxiliary data during training to enhance the accuracy of ultrasonic echo-based depth estimation.
The multi-disciplinary nature of this research is evident in various aspects. Firstly, it combines concepts from the fields of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities. By exploring the potential of inaudible ultrasonic echoes, this research expands the scope of multimedia technologies by introducing a new method for depth estimation. The findings of this study have implications for the development of multimedia applications that incorporate depth sensing capabilities.
Furthermore, the adoption of a deep learning approach demonstrates the integration of artificial intelligence techniques into the field of depth estimation. This fusion of disciplines allows for the development of more accurate and robust depth estimation methods. As deep learning continues to advance, it has the potential to revolutionize the field of multimedia information systems by enabling more sophisticated and adaptive algorithms.
In conclusion, this paper presents a comprehensive study on echo-based depth estimation using inaudible ultrasonic echoes. By addressing the limitations of previous studies, the authors propose a deep learning method that leverages audible echoes during training to improve the accuracy of ultrasonic echo-based depth estimation. The findings of this research contribute to the wider field of multimedia information systems, animations, artificial reality, augmented reality, and virtual realities by introducing a new method for depth estimation and showcasing the potential of deep learning in this domain.
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by jsendak | Jul 31, 2024 | AI
arXiv:2407.19035v1 Announce Type: new Abstract: The creation of high-quality 3D assets is paramount for applications in digital heritage preservation, entertainment, and robotics. Traditionally, this process necessitates skilled professionals and specialized software for the modeling, texturing, and rendering of 3D objects. However, the rising demand for 3D assets in gaming and virtual reality (VR) has led to the creation of accessible image-to-3D technologies, allowing non-professionals to produce 3D content and decreasing dependence on expert input. Existing methods for 3D content generation struggle to simultaneously achieve detailed textures and strong geometric consistency. We introduce a novel 3D content creation framework, ScalingGaussian, which combines 3D and 2D diffusion models to achieve detailed textures and geometric consistency in generated 3D assets. Initially, a 3D diffusion model generates point clouds, which are then densified through a process of selecting local regions, introducing Gaussian noise, followed by using local density-weighted selection. To refine the 3D gaussians, we utilize a 2D diffusion model with Score Distillation Sampling (SDS) loss, guiding the 3D Gaussians to clone and split. Finally, the 3D Gaussians are converted into meshes, and the surface textures are optimized using Mean Square Error(MSE) and Gradient Profile Prior(GPP) losses. Our method addresses the common issue of sparse point clouds in 3D diffusion, resulting in improved geometric structure and detailed textures. Experiments on image-to-3D tasks demonstrate that our approach efficiently generates high-quality 3D assets.
The article “ScalingGaussian: A Novel Framework for High-Quality 3D Content Creation” introduces a new approach to generating 3D assets that combines 3D and 2D diffusion models. Traditionally, creating high-quality 3D assets required skilled professionals and specialized software. However, the increasing demand for 3D assets in gaming and virtual reality has led to the development of accessible image-to-3D technologies that allow non-professionals to create 3D content. Existing methods for 3D content generation often struggle to achieve both detailed textures and strong geometric consistency.
The ScalingGaussian framework addresses this challenge by utilizing a combination of 3D and 2D diffusion models. Initially, a 3D diffusion model generates point clouds, which are then densified through a process of selecting local regions, introducing Gaussian noise, and using local density-weighted selection. To refine the 3D Gaussians, a 2D diffusion model with Score Distillation Sampling (SDS) loss is employed, guiding the 3D Gaussians to clone and split. Finally, the 3D Gaussians are converted into meshes, and the surface textures are optimized using Mean Square Error (MSE) and Gradient Profile Prior (GPP) losses.
By addressing the common issue of sparse point clouds in 3D diffusion, the ScalingGaussian framework improves the geometric structure and detailed textures of generated 3D assets. Experimental results on image-to-3D tasks demonstrate that this approach efficiently generates high-quality 3D assets. Overall, the article highlights the importance of 3D asset creation in various fields and presents a novel framework that overcomes the limitations of existing methods, providing a solution for producing detailed and consistent 3D content.
The Future of 3D Content Creation: Combining AI and Diffusion Models
High-quality 3D assets play a crucial role in various industries, from digital heritage preservation to entertainment and robotics. Traditionally, creating these assets required skilled professionals and specialized software, but the increasing demand for 3D content in gaming and virtual reality has paved the way for accessible image-to-3D technologies. These innovations empower non-professionals to generate 3D content while reducing dependence on expert input.
However, existing methods for 3D content generation face challenges in achieving both detailed textures and strong geometric consistency. This is where ScalingGaussian, a novel 3D content creation framework, comes into play. By combining 3D and 2D diffusion models, ScalingGaussian allows for the generation of highly-detailed textures and consistent geometric structures in 3D assets.
The Process
The framework begins with a 3D diffusion model, which generates point clouds as the initial representation of the 3D asset. To enhance the denseness of the point clouds, the model selects local regions and introduces Gaussian noise. Local density-weighted selection is then utilized to refine the densification process.
In order to further refine the 3D Gaussians and improve their consistency, a 2D diffusion model with Score Distillation Sampling (SDS) loss is employed. The SDS loss guides the 3D Gaussians to clone and split, effectively enhancing their geometric structure.
Finally, the 3D Gaussians are converted into meshes, and the surface textures are optimized using Mean Square Error (MSE) and Gradient Profile Prior (GPP) losses. This ensures that the generated 3D assets not only possess detailed textures but also maintain a high level of geometric consistency.
Benefits and Implications
By addressing the common issue of sparse point clouds in 3D diffusion, ScalingGaussian significantly improves the overall quality of generated 3D assets. Its innovative approach allows for the creation of high-quality 3D content efficiently and effectively.
The implications of this framework are vast. Previously, the creation of detailed 3D assets solely relied on the expertise of professionals with access to specialized software. Now, with accessible image-to-3D technologies like ScalingGaussian, non-professionals can actively participate in the creation process.
Moreover, the convergence of AI and diffusion models opens up new possibilities for the future of 3D content creation. As this technology continues to evolve, we may witness a democratization of the industry, enabling more individuals to contribute to the development of 3D assets across various sectors.
In conclusion, ScalingGaussian revolutionizes 3D content creation by combining AI and diffusion models. Its ability to achieve detailed textures and geometric consistency in generated 3D assets paves the way for a more accessible and inclusive future in industries such as digital heritage preservation, entertainment, and robotics.
The paper titled “ScalingGaussian: A Novel Framework for Efficient and High-Quality 3D Content Creation” introduces a new approach to generating high-quality 3D assets. The authors acknowledge the increasing demand for 3D assets in various fields such as digital heritage preservation, entertainment, and robotics. Traditionally, creating such assets required skilled professionals and specialized software, but the emergence of image-to-3D technologies has made it more accessible to non-professionals.
One of the main challenges in generating 3D content is achieving both detailed textures and strong geometric consistency. Existing methods have struggled to achieve both simultaneously. The proposed framework, ScalingGaussian, aims to address this issue by combining 3D and 2D diffusion models.
The process begins with a 3D diffusion model that generates point clouds. These point clouds are then densified through a process that involves selecting local regions, introducing Gaussian noise, and using local density-weighted selection. This step helps improve the geometric structure of the generated 3D assets.
To refine the 3D Gaussians, a 2D diffusion model with Score Distillation Sampling (SDS) loss is utilized. This step guides the 3D Gaussians to clone and split, further enhancing the geometric consistency. Finally, the 3D Gaussians are converted into meshes, and the surface textures are optimized using Mean Square Error (MSE) and Gradient Profile Prior (GPP) losses.
The experiments conducted on image-to-3D tasks demonstrate that the proposed approach efficiently generates high-quality 3D assets. By addressing the issue of sparse point clouds and utilizing the combination of diffusion models, ScalingGaussian achieves detailed textures and strong geometric consistency.
In terms of potential future developments, it would be interesting to see how the proposed framework performs on more complex and diverse datasets. Additionally, further optimization of the surface textures using advanced techniques could potentially enhance the visual quality of the generated 3D assets. Moreover, the authors could explore the application of their framework in other domains beyond gaming and virtual reality, such as architecture or medical imaging. Overall, ScalingGaussian presents a promising approach to democratizing 3D content creation and has the potential to impact various industries that rely on high-quality 3D assets.
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by jsendak | May 7, 2024 | GR & QC Articles
arXiv:2405.02380v1 Announce Type: new
Abstract: This paper introduces several ideas of emergent gravity, which come from a system similar to an ensemble of quantum spin-$tfrac{1}{2}$ particles. To derive a physically relevant theory, the model is constructed by quantizing a scalar field in curved space-time. The quantization is based on a classical discretization of the system, but contrary to famous approaches, like loop quantum gravity or causal triangulation, a Monte-Carlo based approach is used instead of a simplicial approximation of the space-time manifold. This avoids conceptual issues related to the choice of the lattice. Moreover, this allows us to easily encode the geometric structures of space, given by the geodesic length between points, into the mean value of a correlation operator between two spin-like systems. Numerical investigations show the relevance of the approach, and the presence of two regimes: a classical and a quantum regime. The latter is obtained when the density of points reaches a given threshold. Finally, a multi-scale analysis is given, where the classical model is recovered from the full quantum one. Each step of the classical limit is illustrated with numerical computations, showing the very good convergence towards the classical limit and the computational efficiency of the theory.
Emergent Gravity: Ideas and Insights
This paper introduces the concept of emergent gravity through a system similar to an ensemble of quantum spin-$tfrac{1}{2}$ particles. By quantizing a scalar field in curved space-time, a physically relevant theory is derived, avoiding the limitations of existing approaches like loop quantum gravity or causal triangulation. Instead, a Monte-Carlo based approach is used, allowing for the encoding of geometric structures into the mean value of a correlation operator.
Challenges on the Horizon
- The choice of lattice: The paper addresses conceptual issues related to the choice of the lattice, which have been a challenge in previous approaches. By using a Monte-Carlo based method, these issues are circumvented, providing a more robust framework for emergent gravity.
- Quantum regime threshold: The paper highlights the presence of two regimes – classical and quantum. The transition to the quantum regime depends on the density of points reaching a specific threshold. Further investigations are required to understand the implications of this threshold and its role in the emergence of gravity.
- Computational efficiency: While the paper demonstrates computational efficiency, future implementations and experiments could face challenges in scaling up the system and validating its efficiency in more complex scenarios.
Opportunities for the Future
- Further numerical investigations: The relevance of the approach has been shown through numerical investigations. Future research can focus on exploring different scenarios and systems to validate and expand the findings.
- Integration of observational data: Incorporating observational data from existing and upcoming experiments can help validate the emergent gravity model. Comparing the predictions of the theory with experimental results can uncover new insights and potential avenues for further exploration.
- Applications in cosmology and astrophysics: Emergent gravity has the potential to provide a fresh perspective on cosmological and astrophysical phenomena. Exploring its implications in these fields can shed light on unresolved questions and lead to new discoveries.
Roadmap for Readers
- Understand the limitations of existing approaches to gravity and the conceptual challenges they face.
- Explore the advantages of the introduced Monte-Carlo based approach as a robust framework for emergent gravity.
- Grasp the significance of encoding geometric structures into the mean value of a correlation operator.
- Gain insights into the existence of two regimes – classical and quantum – and the conditions for transition.
- Consider the challenges in scaling up the system and ensure computational efficiency in more complex scenarios.
- Appreciate the relevance of numerical investigations and their potential for further exploration and validation.
- Recognize the importance of integrating observational data and its role in validating the emergent gravity model.
- Explore the potential applications of emergent gravity in cosmology and astrophysics and the opportunities for new discoveries.
Conclusion:
The paper presents a novel perspective on emergent gravity, overcoming conceptual challenges and offering a robust framework through a Monte-Carlo based approach. With further investigation, this approach holds the potential to provide new insights into the nature of gravity, validate against experimental data, and find applications in cosmology and astrophysics.
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by jsendak | Mar 29, 2024 | GR & QC Articles
arXiv:2403.18020v1 Announce Type: new
Abstract: In this paper, we carry out the entanglement calculations on the coherent intertwiners. We first consider the entanglement introduced by the group-averaging of the tensor-product type intertwiner on a four-valents vertex. The result shows that the entanglement is determined by the probability distribution of recoupling spin, and this probability distribution is a well-behaved peak for the highest (and lowest) weight states. Further, we calculated explicitly the entanglement on gauge-invariant coherent intertwiner with four legs. Our numerical results show that the shape of the semiclassical polyhedron described by the coherent intertwiner can be related to the entanglement; In other words, the entanglement is controlled by the face-angle of the semiclassical polyhedron. Finally, we extend our analytical calculation to the coherent intertwiners with arbitrary number of legs.
Entanglement Calculations on Coherent Intertwiners: Conclusions
In this paper, we have conducted entanglement calculations on coherent intertwiners and explored their properties. Our findings have important implications for understanding quantum entanglement and its connection to geometric structures.
Conclusion 1: Entanglement in Tensor-Product Intertwiners
When considering the entanglement introduced by the group-averaging of tensor-product type intertwiners on a four-valent vertex, we have discovered that the entanglement is determined by the probability distribution of recoupling spin. Interestingly, this probability distribution exhibits a well-behaved peak for the highest (and lowest) weight states. This insight provides a deeper understanding of the entanglement phenomenon in these systems.
Conclusion 2: Entanglement in Gauge-Invariant Coherent Intertwiners
We have explicitly calculated the entanglement in gauge-invariant coherent intertwiners with four legs. Our numerical results have revealed a relationship between the shape of the semiclassical polyhedron described by the coherent intertwiner and the entanglement. Specifically, the entanglement is controlled by the face-angle of the semiclassical polyhedron. This connection between geometry and entanglement opens up new avenues for investigation and potential applications.
Conclusion 3: Extending Analytical Calculations to Coherent Intertwiners with Arbitrary Legs
Lastly, we have extended our analytical calculations to coherent intertwiners with an arbitrary number of legs. This allows us to explore entanglement in more complex systems. By understanding how entanglement behaves in these scenarios, we can gain insights into quantum information storage and processing in a broader context.
Future Roadmap and Potential Challenges
Opportunities
- Further investigate the relationship between entanglement and the probability distribution of recoupling spin in tensor-product type intertwiners.
- Explore the connection between geometric properties of semiclassical polyhedra and entanglement in gauge-invariant coherent intertwiners with different numbers of legs.
- Apply knowledge gained from entanglement analysis in coherent intertwiners to quantum information storage and processing in more complex systems.
Challenges
- Developing advanced analytical techniques to calculate entanglement in coherent intertwiners with arbitrary numbers of legs.
- Gaining a deeper understanding of the relationship between entanglement and geometric properties of semiclassical polyhedra.
- Identifying and addressing potential limitations or assumptions in the current entanglement calculations.
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