The field of 3D reconstruction and generation is witnessing significant advancements with the integration of diffusion-based models. These models are being utilized to enhance the accuracy and detail of 3D meshes, reconstruct materials and environments, and generate realistic scenes and images. The use of diffusion models is allowing for the decoupling of global and local generation tasks, enabling the creation of high-fidelity 3D models with rich details. Furthermore, the application of these models in various domains such as text-to-LiDAR scene generation, material reconstruction, and camouflage image generation is demonstrating promising results. Noteworthy papers in this regard include PartDiffuser, which proposes a semi-autoregressive diffusion framework for point-cloud-to-mesh generation, and MatMart, which introduces a novel material reconstruction framework for 3D objects. Additionally, the Text-to-LiDAR Diffusion Model and the Noise-Sparsity-Aware Diffusion Model are showcasing impressive capabilities in generating detailed 3D scenes and enhancing environment reconstruction. Overall, the incorporation of diffusion-based models is pushing the boundaries of 3D reconstruction and generation, enabling the creation of more accurate, detailed, and realistic models.