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😎AI/3D Reconstruction16

[Paper Review] SyncDreamer: Generating Multiview-Consistent Images From a Single-View Image https://arxiv.org/abs/2309.03453 SyncDreamer: Generating Multiview-consistent Images from a Single-view ImageIn this paper, we present a novel diffusion model called that generates multiview-consistent images from a single-view image. Using pretrained large-scale 2D diffusion models, recent work Zero123 demonstrates the ability to generate plausible novel views farxiv.org Abstract이 논문에서는 SyncD.. 2025. 3. 18.

[Paper Review] Wonder3D: Single Image to 3D Using Cross-Domain Diffusion https://arxiv.org/abs/2310.15008 Wonder3D: Single Image to 3D using Cross-Domain DiffusionIn this work, we introduce Wonder3D, a novel method for efficiently generating high-fidelity textured meshes from single-view images.Recent methods based on Score Distillation Sampling (SDS) have shown the potential to recover 3D geometry from 2D diffusionarxiv.org 이 논문에서 제시하는 Wonder3D 는 다중 시점 일관성을 유지하는 법선 .. 2025. 3. 13.

[Paper Review] ThemeStation: Generating Theme-Aware 3D assets from Few Exemplars https://arxiv.org/abs/2403.15383 ThemeStation: Generating Theme-Aware 3D Assets from Few ExemplarsReal-world applications often require a large gallery of 3D assets that share a consistent theme. While remarkable advances have been made in general 3D content creation from text or image, synthesizing customized 3D assets following the shared theme of inarxiv.org 문제 정의 가상현실(VR)이나 비디오 게임에서는 테마적으로.. 2025. 3. 11.

[Notable] 3DGS(3D gaussian splatting) 은 Differentiable 한가? ▶3DGS 는 Explicit Representation (명시적 표현) 방식이지만, 렌더링 과정이 미분 가능하게 설계되어 있어서 학습할 수 있습니다. ✅ Explicit Representation인데 왜 Differentiable 할까?보통 Explicit Representation(명시적 표현)은 3D 객체를 직접적으로 나타내는 방식이기 때문에, 미분이 어렵다고 생각할 수 있습니다.예를 들어:Point Cloud (점 클라우드) → 단순한 3D 좌표 집합이므로 미분이 어려움.Mesh (메쉬, 삼각형 기반 모델) → 버텍스(Vertex)와 페이스(Face)로 표현되며, 일반적인 경우 미분이 쉽지 않음.하지만 3DGS는 Gaussian Splatting을 사용하여 미분 가능(differentiable)한.. 2025. 2. 17.

[Paper Review] Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with Transformers https://arxiv.org/abs/2312.09147 Triplane Meets Gaussian Splatting: Fast and Generalizable Single-View 3D Reconstruction with TransformersRecent advancements in 3D reconstruction from single images have been driven by the evolution of generative models. Prominent among these are methods based on Score Distillation Sampling (SDS) and the adaptation of diffusion models in the 3D domain. Despitarxi.. 2025. 2. 17.

[Paper Review][Workflow Review] DreamFusion: Text-to-3D Using 2D Diffusion DreamFusion 은 Text input에 3D output을 결과로 하는 논문입니다.https://dreamfusion3d.github.io/ DreamFusion: Text-to-3D using 2D DiffusionDreamFusion: Text-to-3D using 2D Diffusion, 2022.dreamfusion3d.github.io 정말 간단하게 정리를 하자면,NeRF 를 사용하여 랜덤한 3D 물체를 렌더합니다.그리고 text 데이터를 input으로 넣으면 Stable Diffusion 을 이용하여 2D image 를 generate 합니다.generated 된 이미지와 NeRF 에서 렌더된 3D 이미지의 2D, 그리고 text prompt 를 SDS (Score Distillati.. 2025. 1. 23.

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