Physics Based Differentiable Rendering for Inverse Problems and Beyond
원문 초록
Physics-based differentiable rendering (PBDR) has become an efficient method in computer vision, graphics, and machine learning for addressing an array of inverse problems. PBDR allows patterns to be generated from perceptions which can be applied to enhance object attributes like geometry, substances, and lighting by adding physical models of light propagation and materials interaction. Due to these capabilities, distinguished rendering has been employed in a wider range of sectors such as autonomous navigation, scene reconstruction, and material design. We provide an extensive overview of PBDR techniques in this study, emphasizing their creation, effectiveness, and limitations while managing inverse situations. We demonstrate modern techniques and examine their value in everyday situations.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
GeoDistill: Geometry-Guided Self-Distillation for Weakly Supervised Cross-View Localization
Improve Underwater Object Detection through YOLOv12 Architecture and Physics-informed Augmentation
VideoMolmo: Spatio-Temporal Grounding Meets Pointing
3D Gaussian Splat Vulnerabilities
GeoDrive: 3D Geometry-Informed Driving World Model with Precise Action Control
MinkUNeXt-SI: Improving point cloud-based place recognition including spherical coordinates and LiDAR intensity
토론
이 논문과 연결된 포럼 글이 아직 없습니다.