Figure 5 From Single View 3d Scene Reconstruction With High Fidelity Shape And Texture

By switzerlandersing On Sep 12, 2025

Single-view 3D Scene Reconstruction With High-fidelity Shape And Texture

Single-view 3D Scene Reconstruction With High-fidelity Shape And Texture To address these challenges, we propose a novel framework for simultaneous high fidelity recovery of object shapes and textures from single view images. We compare our reconstruction with state of the art models in single view 3d reconstruction. our model produces textured 3d objects with smoother surfaces and finer details compared to previous methods.

GitHub - Nihalsid/single-view-3d-reconstruction

GitHub - Nihalsid/single-view-3d-reconstruction A distinctive feature of our framework is its ability to generate fine grained textured meshes while seamlessly integrating rendering capabilities into the single view 3d reconstruction model. Our framework leverages the strengths of neural implicit surfaces in shape learning and radiance fields in texture modeling, and seamlessly introduces rendering capabilities into a single view 3d reconstruction model. This paper presents a novel pipeline for estimating room layouts and reconstructing the 3d shapes of indoor objects. this task remains challenging due to occlus. To address these challenges, we propose a novel framework for simultaneous high fidelity recovery of object shapes and textures from single view images.

[3DV24] Single-view 3D Scene Reconstruction With High-fidelity Shape And Texture | PKU CoRe Lab

[3DV24] Single-view 3D Scene Reconstruction With High-fidelity Shape And Texture | PKU CoRe Lab This paper presents a novel pipeline for estimating room layouts and reconstructing the 3d shapes of indoor objects. this task remains challenging due to occlus. To address these challenges, we propose a novel framework for simultaneous high fidelity recovery of object shapes and textures from single view images. Figure 5. comparison with prior guided models. inputs for zero 1 to 3 [42] and shap¨e [33] only contains foreground objects. each example is presented with textured mesh and mesh from three views. our method outperforms prior guided models in capturing details and 3d shape consistency. Figure 1: single view 3d scene reconstruction with high fidelity shape and texture. (a) object level and (b) scene level reconstruction. rendering of color, depth, and normal images from the original and novel viewpoints enables 3d scene editing. (c) object manipulation by rotating the object in (a) and scene composition of (a) and (b). \difaddend. In this work we present a novel approach that leverages recent advances in 2d 3d lifting using neural shape priors while also enforcing multi view equivariance. This paper proposes a novel framework to simultaneously recover detailed 3d geometry and realistic textures of objects and scenes from single rgb images. it represents objects using neural implicit shape and radiance fields, benefiting from both 3d supervision and volume rendering.

Single Image 3D Scene Reconstruction: A Review Of Recent Advances | HackerNoon

Single Image 3D Scene Reconstruction: A Review Of Recent Advances | HackerNoon Figure 5. comparison with prior guided models. inputs for zero 1 to 3 [42] and shap¨e [33] only contains foreground objects. each example is presented with textured mesh and mesh from three views. our method outperforms prior guided models in capturing details and 3d shape consistency. Figure 1: single view 3d scene reconstruction with high fidelity shape and texture. (a) object level and (b) scene level reconstruction. rendering of color, depth, and normal images from the original and novel viewpoints enables 3d scene editing. (c) object manipulation by rotating the object in (a) and scene composition of (a) and (b). \difaddend. In this work we present a novel approach that leverages recent advances in 2d 3d lifting using neural shape priors while also enforcing multi view equivariance. This paper proposes a novel framework to simultaneously recover detailed 3d geometry and realistic textures of objects and scenes from single rgb images. it represents objects using neural implicit shape and radiance fields, benefiting from both 3d supervision and volume rendering.

Example Of Single-view 3D Object Reconstruction On Pix3D At 32 3... | Download Scientific Diagram

Example Of Single-view 3D Object Reconstruction On Pix3D At 32 3... | Download Scientific Diagram In this work we present a novel approach that leverages recent advances in 2d 3d lifting using neural shape priors while also enforcing multi view equivariance. This paper proposes a novel framework to simultaneously recover detailed 3d geometry and realistic textures of objects and scenes from single rgb images. it represents objects using neural implicit shape and radiance fields, benefiting from both 3d supervision and volume rendering.