Papers with Code 아카이브
Scene Segmentation
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Scene segmentation is the task of splitting a scene into its various object components.
Image adapted from Temporally coherent 4D reconstruction of complex dynamic scenes.
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/anheidelonghu/ACNet
공식 코드ACNet: Attention Based Network to Exploit Complementary Features for RGBD Semantic Segmentation
https://github.com/atapour/temporal-depth-segmentation
공식 코드https://github.com/aymanbegh/cd-coco
공식 코드CD-COCO: A Versatile Complex Distorted COCO Database for Scene-Context-Aware Computer Vision
https://github.com/camma-public/endoscapes
공식 코드https://github.com/cchen-cc/ma-sam
공식 코드MA-SAM: Modality-agnostic SAM Adaptation for 3D Medical Image Segmentation
https://github.com/cheliosoops/bitq
공식 코드https://github.com/chen742/DCF
공식 코드Transferring to Real-World Layouts: A Depth-aware Framework for Scene Adaptation
https://github.com/chenfengxu714/image2point
공식 코드Image2Point: 3D Point-Cloud Understanding with 2D Image Pretrained Models
https://github.com/cocodataset/panopticapi
공식 코드https://github.com/codename1995/APNet_ICCVW23
공식 코드APNet: Urban-level Scene Segmentation of Aerial Images and Point Clouds
https://github.com/csiplab/mmsformer
공식 코드MMSFormer: Multimodal Transformer for Material and Semantic Segmentation
https://github.com/cxjyxxme/pix2pixSC
공식 코드Example-Guided Style Consistent Image Synthesis from Semantic Labeling
https://github.com/cyuan-sjtu/vit-asym
공식 코드Surgical Scene Segmentation by Transformer With Asymmetric Feature Enhancement
https://github.com/dong-huo/rgb-t-glass-segmentation
공식 코드https://github.com/dvssajay/PDFNet-Pointwise-Dense-Flow-Network-for-Urban-Scene-Segmentation
공식 코드PDFNet: Pointwise Dense Flow Network for Urban-Scene Segmentation
https://github.com/eceo-epfl/coralscapesScripts
공식 코드The Coralscapes Dataset: Semantic Scene Understanding in Coral Reefs
https://github.com/eceo-epfl/mammalps
공식 코드MammAlps: A multi-view video behavior monitoring dataset of wild mammals in the Swiss Alps
https://github.com/enyahermite/picasso
공식 코드Mesh Convolution with Continuous Filters for 3D Surface Parsing
https://github.com/ethz-asl/autolabel
공식 코드https://github.com/facebookresearch/detectron2
공식 코드https://github.com/fcdl94/RobotChallenge
공식 코드The RGB-D Triathlon: Towards Agile Visual Toolboxes for Robots
https://github.com/freeformrobotics/eaefnet
공식 코드Explicit Attention-Enhanced Fusion for RGB-Thermal Perception Tasks
https://github.com/ganlumomo/thermal-uda-attention
공식 코드Unsupervised RGB-to-Thermal Domain Adaptation via Multi-Domain Attention Network
https://github.com/greekinroma/kmndnet
공식 코드https://github.com/gy20073/BDD_Driving_Model
공식 코드End-to-end Learning of Driving Models from Large-scale Video Datasets
https://github.com/hanchaoleng/shapeconv
공식 코드ShapeConv: Shape-aware Convolutional Layer for Indoor RGB-D Semantic Segmentation
https://github.com/hehao13/EBLNet
공식 코드Enhanced Boundary Learning for Glass-like Object Segmentation
https://github.com/henghuiding/BFP
공식 코드https://github.com/hlei-ziyan/Picasso
공식 코드Mesh Convolution with Continuous Filters for 3D Surface Parsing
https://github.com/hlwang1124/AAFramework
공식 코드We Learn Better Road Pothole Detection: from Attention Aggregation to Adversarial Domain Adaptation
https://github.com/hlwang1124/SNE-RoadSeg
공식 코드https://github.com/hrishavbakulbarua/gta-hdr
공식 코드GTA-HDR: A Large-Scale Synthetic Dataset for HDR Image Reconstruction
https://github.com/hszhao/ICNet
공식 코드ICNet for Real-Time Semantic Segmentation on High-Resolution Images
https://github.com/hszhao/PSPNet
공식 코드https://github.com/huaaaliu/rgbx_semantic_segmentation
공식 코드CMX: Cross-Modal Fusion for RGB-X Semantic Segmentation with Transformers
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.