SceneDreamer360: Text-Driven 3D-Consistent Scene Generation with Panoramic Gaussian Splatting

Text-driven 3D scene generation has seen significant advancements recently. However, most existing methods generate single-view images using generative models and then stitch them together in 3D space. This independent generation for each view often results in spatial inconsistency and implausibility in the 3D scenes. To address this challenge, we proposed a novel text-driven 3D-consistent scene generation model: SceneDreamer360. Our proposed method leverages a text-driven panoramic image generation model as a prior for 3D scene generation and employs 3D Gaussian Splatting (3DGS) to ensure consistency across multi-view panoramic images. Specifically, SceneDreamer360 enhances the fine-tuned Panfusion generator with a three-stage panoramic enhancement, enabling the generation of high-resolution, detail-rich panoramic images. During the 3D scene construction, a novel point cloud fusion initialization method is used, producing higher quality and spatially consistent point clouds. Our extensive experiments demonstrate that compared to other methods, SceneDreamer360 with its panoramic image generation and 3DGS can produce higher quality, spatially consistent, and visually appealing 3D scenes from any text prompt. Our codes are available at \url{https://github.com/liwrui/SceneDreamer360}.

Riemann-based Multi-scale Attention Reasoning Network for Text-3D Retrieval

Due to the challenges in acquiring paired Text-3D data and the inherent irregularity of 3D data structures, combined representation learning of 3D point clouds and text remains unexplored. In this paper, we propose a novel Riemann-based Multi-scale Attention Reasoning Network (RMARN) for text-3D retrieval. Specifically, the extracted text and point cloud features are refined by their respective Adaptive Feature Refiner (AFR). Furthermore, we introduce the innovative Riemann Local Similarity (RLS) module and the Global Pooling Similarity (GPS) module. However, as 3D point cloud data and text data often possess complex geometric structures in high-dimensional space, the proposed RLS employs a novel Riemann Attention Mechanism to reflect the intrinsic geometric relationships of the data. Without explicitly defining the manifold, RMARN learns the manifold parameters to better represent the distances between text-point cloud samples. To address the challenges of lacking paired text-3D data, we have created the large-scale Text-3D Retrieval dataset T3DR-HIT, which comprises over 3,380 pairs of text and point cloud data. T3DR-HIT contains coarse-grained indoor 3D scenes and fine-grained Chinese artifact scenes, consisting of 1,380 and over 2,000 text-3D pairs, respectively. Experiments on our custom datasets demonstrate the superior performance of the proposed method. Our code and proposed datasets are available at \url{https://github.com/liwrui/RMARN}.

Deep Network for Image Compressed Sensing Coding Using Local Structural Sampling

Existing image compressed sensing (CS) coding frameworks usually solve an inverse problem based on measurement coding and optimization-based image reconstruction, which still exist the following two challenges: 1) The widely used random sampling matrix, such as the Gaussian Random Matrix (GRM), usually leads to low measurement coding efficiency. 2) The optimization-based reconstruction methods generally maintain a much higher computational complexity. In this paper, we propose a new CNN based image CS coding framework using local structural sampling (dubbed CSCNet) that includes three functional modules: local structural sampling, measurement coding and Laplacian pyramid reconstruction. In the proposed framework, instead of GRM, a new local structural sampling matrix is first developed, which is able to enhance the correlation between the measurements through a local perceptual sampling strategy. Besides, the designed local structural sampling matrix can be jointly optimized with the other functional modules during training process. After sampling, the measurements with high correlations are produced, which are then coded into final bitstreams by the third-party image codec. At last, a Laplacian pyramid reconstruction network is proposed to efficiently recover the target image from the measurement domain to the image domain. Extensive experimental results demonstrate that the proposed scheme outperforms the existing state-of-the-art CS coding methods, while maintaining fast computational speed.

A Memory-Related Multi-Task Method Based on Task-Agnostic Exploration

We pose a new question: Can agents learn how to combine actions from previous tasks to complete new tasks, just as humans? In contrast to imitation learning, there is no expert data, only the data collected through environmental exploration. Compared with offline reinforcement learning, the problem of data distribution shift is more serious. Since the action sequence to solve the new task may be the combination of trajectory segments of multiple training tasks, in other words, the test task and the solving strategy do not exist directly in the training data. This makes the problem more difficult. We propose a Memory-related Multi-task Method (M3) to address this problem. The method consists of three stages. First, task-agnostic exploration is carried out to collect data. Different from previous methods, we organize the exploration data into a knowledge graph. We design a model based on the exploration data to extract action effect features and save them in memory, while an action predictive model is trained. Secondly, for a new task, the action effect features stored in memory are used to generate candidate actions by a feature decomposition-based approach. Finally, a multi-scale candidate action pool and the action predictive model are fused to generate a strategy to complete the task. Experimental results show that the performance of our proposed method is significantly improved compared with the baseline.