3D (12) 썸네일형 리스트형 [DiT-3D or DDPM Code 분석] Github Linkhttps://github.com/DiT-3D/DiT-3D/blob/main/train.py DiT-3D/train.py at main · DiT-3D/DiT-3D🔥🔥🔥Official Codebase of "DiT-3D: Exploring Plain Diffusion Transformers for 3D Shape Generation" - DiT-3D/DiT-3Dgithub.com*매우매우 글이 긴 초장문입니다..!각 코드별로 엄청 상세하게 리뷰했고, 최대한 흐름에 따라서 코드와 수식을 붙여서 설명하였습니다.*DiT-3D 코드를 기반으로 설명하고 있지만, dataloader를 제외한 나머지 리뷰는 2D 기반의 DDPM or Diffusion Transformer 코드의 흐름으로 이.. [MeshAnything 논문 리뷰] - MeshAnything: Artist-Created Mesh Generation with Autoregressive Transformers *MeshAnything를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! MeshAnything paper: https://arxiv.org/abs/2406.10163 MeshAnything: Artist-Created Mesh Generation with Autoregressive TransformersRecently, 3D assets created via reconstruction and generation have matched the quality of manually crafted assets, highlighting their potential for replacement. However, this potential is largely unrealized because thes.. [LGM 논문 리뷰] Large Multi-View Gaussian Model for High-Resolution 3D Content Creation *LGM를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! LGM github: LGM (kiui.moe) LGMLGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation Arxiv 2024 Jiaxiang Tang1, Zhaoxi Chen2, Xiaokang Chen1, Tengfei Wang3, Gang Zeng1, Ziwei Liu2 1 Peking University 2 S-Lab, Nanyang Technological University 3 Shanghai AI Lame.kiui.moeContents1. Simple Introduction2. Background Knowledge: Gaussia.. [LRM 논문 리뷰] - LARGE RECONSTRUCTION MODEL FOR SINGLE IMAGE TO 3D *LRM를 위한 논문 리뷰 글입니다! 궁금하신 점은 댓글로 남겨주세요! LRM paper: https://arxiv.org/abs/2311.04400 LRM: Large Reconstruction Model for Single Image to 3DWe propose the first Large Reconstruction Model (LRM) that predicts the 3D model of an object from a single input image within just 5 seconds. In contrast to many previous methods that are trained on small-scale datasets such as ShapeNet in a category-specarxi.. [SMPL-X Implementation] KyujinHan/Smplify-X-Perfect-Implementation Github: https://github.com/KyujinHan/Smplify-X-Perfect-Implementation GitHub - KyujinHan/Smplify-X-Perfect-Implementation: Smplify-X implementation. (2024. 03. 18 No Error & Recent version) Smplify-X implementation. (2024. 03. 18 No Error & Recent version) - KyujinHan/Smplify-X-Perfect-Implementation github.com Smplify-X Implementation (recent version) SMPL-X를 예전에 구현한 적이 있었는데, 코드가 다시 날아가서 다시 구현하.. [Swin UNETR 논문 리뷰] - Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images *해당 글은 Swin UNETR 논문 리뷰를 위한 글입니다. 궁금하신 점은 댓글로 남겨주세요. Swin UNETR: [2201.01266] Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images (arxiv.org) Swin UNETR: Swin Transformers for Semantic Segmentation of Brain Tumors in MRI Images Semantic segmentation of brain tumors is a fundamental medical image analysis task involving multiple MRI imaging modalities that can as.. [ViT for NeRF 논문 리뷰] - Vision Transformer for NeRF-Based View Synthesis from a Single Input Image *해당논문은 Vision Transformer for NeRF를 위한 논문 리뷰 글입니다! 궁금한 점은 댓글로 남겨주세요! Vision Transformer for NeRF paper: [2207.05736] Vision Transformer for NeRF-Based View Synthesis from a Single Input Image (arxiv.org) Vision Transformer for NeRF-Based View Synthesis from a Single Input Image Although neural radiance fields (NeRF) have shown impressive advances for novel view synthesis, most methods typically .. [FissureNet 논문 리뷰] - FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images * 해당 글은 논문 리뷰를 위한 글이고, 궁금하신 점이 있다면 댓글로 남겨주세요! FissureNet paper: FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images - PMC (nih.gov) FissureNet: A Deep Learning Approach For Pulmonary Fissure Detection in CT Images Pulmonary fissure detection in computed tomography (CT) is a critical component for automatic lobar segmentation. The majority of fissure detection method.. [UNETR++ 논문 리뷰] - UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation *UNETR++ 논문 리뷰를 위한 글입니다. 질문이 있다면 댓글로 남겨주시길 바랍니다! UNETR++ paper: [2212.04497] UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation (arxiv.org) UNETR++: Delving into Efficient and Accurate 3D Medical Image Segmentation Owing to the success of transformer models, recent works study their applicability in 3D medical segmentation tasks. Within the transformer models, the self-at.. [UNETR 논문 리뷰] - UNETR: Transformers for 3D Medical Image Segmentation *UNETR 논문 리뷰를 위한 글이고, 질문이 있으시다면 언제든지 댓글로 남겨주세요! UNETR paper: [2103.10504] UNETR: Transformers for 3D Medical Image Segmentation (arxiv.org) UNETR: Transformers for 3D Medical Image Segmentation Fully Convolutional Neural Networks (FCNNs) with contracting and expanding paths have shown prominence for the majority of medical image segmentation applications since the past decade. In FCNNs, the enco.. [TransUNet 논문 리뷰] - TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation *TransUNet 논문 리뷰를 위한 글이고, 질문이 있으시다면 언제든지 댓글로 남겨주세요! TransUNet paper: [2102.04306] TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation (arxiv.org) TransUNet: Transformers Make Strong Encoders for Medical Image Segmentation Medical image segmentation is an essential prerequisite for developing healthcare systems, especially for disease diagnosis and treatment planning. On v.. [(3D) U-Net 논문 리뷰] - 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation *U-Net 논문 리뷰를 위한 글이고, 질문이 있으시다면 언제든지 댓글로 남겨주세요! 3D U-Net paper: [1606.06650] 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation (arxiv.org) 3D U-Net: Learning Dense Volumetric Segmentation from Sparse Annotation This paper introduces a network for volumetric segmentation that learns from sparsely annotated volumetric images. We outline two attractive use cases of this method.. 이전 1 다음
