MVDiff: Scalable and Flexible Multi-View Diffusion for 3D Object Reconstruction from Single-View
원문 초록
Generating consistent multiple views for 3D reconstruction tasks is still a challenge to existing image-to-3D diffusion models. Generally, incorporating 3D representations into diffusion model decrease the model's speed as well as generalizability and quality. This paper proposes a general framework to generate consistent multi-view images from single image or leveraging scene representation transformer and view-conditioned diffusion model. In the model, we introduce epipolar geometry constraints and multi-view attention to enforce 3D consistency. From as few as one image input, our model is able to generate 3D meshes surpassing baselines methods in evaluation metrics, including PSNR, SSIM and LPIPS.
함께 살펴볼 논문
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HuSc3D: Human Sculpture dataset for 3D object reconstruction
SR3D: Unleashing Single-view 3D Reconstruction for Transparent and Specular Object Grasping
Direct3D-S2: Gigascale 3D Generation Made Easy with Spatial Sparse Attention
Digital Twin Catalog: A Large-Scale Photorealistic 3D Object Digital Twin Dataset
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Object-X: Learning to Reconstruct Multi-Modal 3D Object Representations
토론
이 논문과 연결된 포럼 글이 아직 없습니다.