Large-scale Neural Radiance Fields (NeRF) reconstructions are typically hindered by the requirement for extensive image datasets and substantial computational resources. This paper introduces IOVS4NeRF, a framework that employs an uncertainty-guided incremental optimal view selection strategy adaptable to various NeRF implementations. Specifically, by leveraging a hybrid uncertainty model that combines rendering and positional uncertainties, the proposed method calculates the most informative view from among the candidates, thereby enabling incremental optimization of scene reconstruction. Our detailed experiments demonstrate that IOVS4NeRF achieves high-fidelity NeRF reconstruction with minimal computational resources, making it suitable for large-scale scene applications.

3D Gaussian Splatting (3DGS) has emerged as a powerful technique for novel view synthesis, demonstrating remarkable capability in high-fidelity scene reconstruction through its Gaussian primitive representations. However, the computational overhead induced by the massive number of primitives poses a significant bottleneck to training efficiency. To overcome this challenge, we propose Group Training, a simple yet effective strategy that organizes Gaussian primitives into manageable groups, optimizing training efficiency and improving rendering quality. This approach shows universal compatibility with existing 3DGS frameworks, including vanilla 3DGS and Mip-Splatting, consistently achieving accelerated training while maintaining superior synthesis quality. Extensive experiments reveal that our straightforward Group Training strategy achieves up to 30\% faster convergence and improved rendering quality across diverse scenarios. Project Website: https://chengbo-wang.github.io/3DGS-with-Group-Training/

Recently, MGTV organized the Image-to-Video Model Acceleration Challenge, calling for participants to propose optimization solutions for the Wan 2.1-14B model. The challenge emphasizes techniques such as quantization and GPU acceleration to improve the model's inference efficiency. As AIGC technology advances rapidly, video generation large models exhibit great potential in content creation, yet they face critical challenges of high computing power consumption, long inference time, and excessive VRAM usage during inference, which severely hinder content production efficiency. This challenge aims to explore approaches for efficient video generation under limited computing resources, requiring participants to reduce the model's computing power and VRAM demands while improving inference speed, all without compromising generation quality. To support participants' development and evaluation, the challenge provides a baseline framework and test dataset. For further details, please refer to the official challenge website (https://challenge.ai.mgtv.com/#/track/53).

The AI Generated Image Detection Challenge, organized by MGTV, invites participants to develop advanced algorithms capable of accurately distinguishing between real and AI-generated images. These images may be created using various cutting-edge techniques, including but not limited to GAN and Stable Diffusion algorithms. Participants are encouraged to utilize open-source datasets or develop their own datasets to train their algorithms. This challenge presents a unique opportunity to enhance the field of AI-generated image detection, particularly in improving the algorithm's generalization capabilities to identify unknown and emerging samples. For more details and resources, please visit our official website (https://challenge.ai.mgtv.com/#/track/24).

We propose an accurate and fast bundle adjustment (BA) solution that estimates the 6-DoF pose with an independent RS model of the camera and the geometry of the environment based on measurements from a rolling shutter (RS) camera. This tackles the challenges in the existing works, namely, relying on high frame rate video as input, restrictive assumptions on camera motion and poor efficiency. To this end, we first verify the positive influence of the image point normalization to RSBA. Then we present a novel visual residual covariance model to standardize the reprojection error during RSBA, which consequently improves the overall accuracy. Besides, we demonstrate the combination of Normalization and covariance standardization Weighting in RSBA (NW-RSBA) can avoid common planar degeneracy without the need to constrain the filming manner. Finally, we propose an acceleration strategy for NW-RSBA based on the sparsity of its Jacobian matrix and Schur complement. The extensive synthetic and real data experiments verify the effectiveness and efficiency of the proposed solution over the state-of-the-art works.

MGTV recently organized a pioneering Invisible Video Watermark Challenge, inviting participants to create a framework capable of embedding invisible watermarks into videos and extracting them from watermarked content.

Numerous web services with the same function but different service qualities are constantly emerging on the network. Optimizing web service composition based on multiple candidate services sets an urgent problem in the service composition neighborhood. This paper modifies the traditional Firefly algorithm and adds exchange and mutation mechanisms to optimize the Web service composition efficiently in multiple candidate service sets. Meanwhile, it discretizes the continuous space of its solution set and better adapts to the service composition optimization problem. Experimental results show that compared with the GA, IA, SA, ACO, FACO, and EFACO algorithms, this algorithm has better optimization performance, faster speed, and higher energy efficiency for solving service composition optimization problems in the case of large-scale data. The higher the combined complexity of the solution, the stronger the performance compared to other algorithms. It can better deal with the increasingly complex situation of Web service composition problems.

Crime analysis uses past crime data to predict future crime locations and times. Typically this analysis relies on hot spot models that show clusters of criminal events based on past locations of these events. It does not consider the decision making processes of criminals as human initiated events susceptible to analysis using spatial choice models. This paper analyzes criminal incidents as spatial choice processes. Spatial choice analysis can be used to discover the distribution of people's behaviors in space and time. Two adjusted spatial choice models that include models of decision making processes are presented. The comparison results show that adjusted spatial choice models provide efficient and accurate predictions of future crime patterns and can be used as the basis for a law enforcement decision support system. This paper also extends spatial choice modeling to include the class of problems where the decision makers' preferences are derived indirectly through incident reports rather than directly through survey instruments.

The discovery of preferences in space and time is important in a variety of applications. In this paper we first establish the correspondence between a set of preferences in space and time and density estimates obtained from observations of spatial-temporal features recorded within large databases. We perform density estimation using both kernel methods and mixture models. The density estimates constitute a probabilistic representation of preferences. We then present a point process transition density model for space-time event prediction that hinges upon the density estimates from the preference discovery process. The added dimension of preference discovery through feature space analysis enables our model to outperform traditional preference modeling approaches. We demonstrate this performance improvement using a criminal incident database from Richmond, Virginia. Criminal incidents are human-initiated events that may be governed by criminal preferences over space and time. We applied our modeling technique to breaking and entering crimes committed in both residential and commercial settings. Our approach effectively recovers the preference structure of the criminals and enables one-week ahead forecasts of threatened areas. This capability to accommodate all measurable features, identify the key features, and quantify their relationship with event occurrence over space and time makes this approach applicable to domains other than law enforcement.