https://github.com/728388808/alpha-neus
공식 코드From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $α$-NeuS
태스크의 하위 분류와 논문·코드·평가·기법·데이터셋을 탐색하세요.
Detecting transparent objects in 2D or 3D
관련 논문과 평가 결과에 연결된 구현입니다. 출처에서 확인된 저자 구현에는 공식 코드 표시가 있습니다.
From Transparent to Opaque: Rethinking Neural Implicit Surfaces with $α$-NeuS
Multi-Label Stereo Matching for Transparent Scene Depth Estimation
NeuralLabeling: A versatile toolset for labeling vision datasets using Neural Radiance Fields
RGB-D Local Implicit Function for Depth Completion of Transparent Objects
ClearGrasp: 3D Shape Estimation of Transparent Objects for Manipulation
SimNet: Enabling Robust Unknown Object Manipulation from Pure Synthetic Data via Stereo
TransMatting: Enhancing Transparent Objects Matting with Transformers
Exploring the Interactive Guidance for Unified and Effective Image Matting
KeyPose: Multi-View 3D Labeling and Keypoint Estimation for Transparent Objects
TOM-Net: Learning Transparent Object Matting from a Single Image
TranSplat: Surface Embedding-guided 3D Gaussian Splatting for Transparent Object Manipulation
Through the Looking Glass: Neural 3D Reconstruction of Transparent Shapes
ClearPose: Large-scale Transparent Object Dataset and Benchmark
Polarimetric Inverse Rendering for Transparent Shapes Reconstruction
Trans2k: Unlocking the Power of Deep Models for Transparent Object Tracking
TDCNet: Transparent Objects Depth Completion with CNN-Transformer Dual-Branch Parallel Network
Spider: A Unified Framework for Context-dependent Concept Segmentation
TODE-Trans: Transparent Object Depth Estimation with Transformer
TOM-Net: Learning Transparent Object Matting from a Single Image
출처: Papers with Code · CC BY-SA 4.0. 아카이브 자료와 새로 수집한 논문은 별도로 관리합니다.