[논문] NeRF: Representing Scenes as Neural Radiance Fields for View Synthesis
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Paper Review
https://www.matthewtancik.com/nerf NeRF: Neural Radiance FieldsA method for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene function using a sparse set of input views.www.matthewtancik.com AbstractWe present a method that achieves SOTA results for synthesizing novel views of complex scenes by optimizing an underlying continuous volumetric scene ..
[논문] 3D Gaussian Splatting for Real-Time Radiance Field Rendering
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Paper Review
3D Gaussian Splatting for Real-Time Radiance Field Rendering[Müller 2022] Müller, T., Evans, A., Schied, C. and Keller, A., 2022. Instant neural graphics primitives with a multiresolution hash encoding [Hedman 2018] Hedman, P., Philip, J., Price, T., Frahm, J.M., Drettakis, G. and Brostow, G., 2018. Deep blendingrepo-sam.inria.frAbstractRadiance Field method : 여러 장의 이미지나 비디오로 novel-view synthesi..
[논문] Grounded SAM: Assembling Open-World Models for Diverse Visual Tasks
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Paper Review
https://github.com/IDEA-Research/Grounded-Segment-Anything GitHub - IDEA-Research/Grounded-Segment-Anything: Grounded SAM: Marrying Grounding DINO with Segment Anything & Stable DiffusionGrounded SAM: Marrying Grounding DINO with Segment Anything & Stable Diffusion & Recognize Anything - Automatically Detect , Segment and Generate Anything - IDEA-Research/Grounded-Segment-A...github.comAbstractO..
[논문] Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection
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Paper Review
https://github.com/IDEA-Research/GroundingDINO GitHub - IDEA-Research/GroundingDINO: [ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Groun[ECCV 2024] Official implementation of the paper "Grounding DINO: Marrying DINO with Grounded Pre-Training for Open-Set Object Detection" - IDEA-Research/GroundingDINOgithub.comhttps://arxiv.org/abs/2303.05499 Grounding DIN..
[논문] SAM: Segment Anything
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Paper Review
https://arxiv.org/abs/2304.02643 Segment AnythingWe introduce the Segment Anything (SA) project: a new task, model, and dataset for image segmentation. Using our efficient model in a data collection loop, we built the largest segmentation dataset to date (by far), with over 1 billion masks on 11M licensearxiv.orgSegment Anything Segment AnythingMeta AI Computer Vision Researchsegment-anything.co..
[논문] Swin Transformer: Hierarchical Vision Transformer using Shifted Windows
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Paper Review
https://arxiv.org/abs/2103.14030 Swin Transformer: Hierarchical Vision Transformer using Shifted WindowsThis paper presents a new vision Transformer, called Swin Transformer, that capably serves as a general-purpose backbone for computer vision. Challenges in adapting Transformer from language to vision arise from differences between the two domains, such asarxiv.org이번 포스팅은 2021 ICCV에 accept된 Sw..