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3D Semantic Occupancy Prediction
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Uses sparse LiDAR semantic labels for training and testing
코드
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
https://github.com/alphaplustt/daocc
공식 코드DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction
https://github.com/cdb342/alocc
공식 코드ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction
https://github.com/cdb342/gdfusion
공식 코드Rethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction
https://github.com/danielming123/inversematrixvt3d
공식 코드InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction
https://github.com/danielming123/occfusion
공식 코드OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Prediction
https://github.com/huang-yh/gaussianformer
공식 코드GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Prediction
https://github.com/hustvl/gausstr
공식 코드GaussTR: Foundation Model-Aligned Gaussian Transformer for Self-Supervised 3D Spatial Understanding
https://github.com/iceory/reo
공식 코드Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial Transformation
https://github.com/northsummer/sliceocc
공식 코드SliceOcc: Indoor 3D Semantic Occupancy Prediction with Vertical Slice Representation
https://github.com/phi-wol/hydra
공식 코드Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception
https://github.com/wzzheng/pointocc
공식 코드PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction
https://github.com/zhangyp15/occformer
공식 코드OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction
https://github.com/zuosc19/quadricformer
공식 코드QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.