arXiv:2110.06199
ABO: Dataset and Benchmarks for Real-World 3D Object Understanding
원문 초록
We introduce Amazon Berkeley Objects (ABO), a new large-scale dataset designed to help bridge the gap between real and virtual 3D worlds. ABO contains product catalog images, metadata, and artist-created 3D models with complex geometries and physically-based materials that correspond to real, household objects. We derive challenging benchmarks that exploit the unique properties of ABO and measure the current limits of the state-of-the-art on three open problems for real-world 3D object understanding: single-view 3D reconstruction, material estimation, and cross-domain multi-view object retrieval.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
AutoPartGen: Autogressive 3D Part Generation and Discovery
DreamGrasp: Zero-Shot 3D Multi-Object Reconstruction from Partial-View Images for Robotic Manipulation
Dare to Plagiarize? Plagiarized Painting Recognition and Retrieval
Mask-aware Text-to-Image Retrieval: Referring Expression Segmentation Meets Cross-modal Retrieval
SAMURAI: Shape-Aware Multimodal Retrieval for 3D Object Identification
Multimodal Information Retrieval for Open World with Edit Distance Weak Supervision
토론
이 논문과 연결된 포럼 글이 아직 없습니다.