3D-FRONT: 3D Furnished Rooms with layOuts and semaNTics
원문 초록
We introduce 3D-FRONT (3D Furnished Rooms with layOuts and semaNTics), a new, large-scale, and comprehensive repository of synthetic indoor scenes highlighted by professionally designed layouts and a large number of rooms populated by high-quality textured 3D models with style compatibility. From layout semantics down to texture details of individual objects, our dataset is freely available to the academic community and beyond. Currently, 3D-FRONT contains 18,968 rooms diversely furnished by 3D objects, far surpassing all publicly available scene datasets. In addition, the 13,151 furniture objects all come with high-quality textures. While the floorplans and layout designs are directly sourced from professional creations, the interior designs in terms of furniture styles, color, and textures have been carefully curated based on a recommender system we develop to attain consistent styles as expert designs. Furthermore, we release Trescope, a light-weight rendering tool, to support benchmark rendering of 2D images and annotations from 3D-FRONT. We demonstrate two applications, interior scene synthesis and texture synthesis, that are especially tailored to the strengths of our new dataset. The project page is at: https://tianchi.aliyun.com/specials/promotion/alibaba-3d-scene-dataset.
함께 살펴볼 논문
공통 태그를 근거로 연결했습니다. 실제 인용 관계나 논문의 우수성을 뜻하지 않습니다.
A Reproducibility Study of Product-side Fairness in Bundle Recommendation
NLGCL: Naturally Existing Neighbor Layers Graph Contrastive Learning for Recommendation
Boosting Parameter Efficiency in LLM-Based Recommendation through Sophisticated Pruning
Kamae: Bridging Spark and Keras for Seamless ML Preprocessing
FindRec: Stein-Guided Entropic Flow for Multi-Modal Sequential Recommendation
Thought-Augmented Planning for LLM-Powered Interactive Recommender Agent
토론
이 논문과 연결된 포럼 글이 아직 없습니다.