Learning 3d Scene Priors With 2d Supervision Cvpr 2023о Figure 1. we learn 3d scene priors with 2d supervision. we model a latent hypersphere surface to represent a manifold of 3d scenes, characterizing the semantic and geometric distribution of objects in 3d scenes. this supports many downstream applica tions, including scene synthesis, interpolation and single view re construction. more accessible. Project: yinyunie.github.io sceneprior page holistic 3d scene understanding entails estimation of both layout configuration and object geometry in a.
Learning 3d Scene Priors With 2d Supervision Holistic 3d scene understanding entails estimation of both layout configuration and object geometry in a 3d environment. recent works have shown advances in 3d scene estimation from various input modalities (e.g., images, 3d scans), by leveraging 3d supervision (e.g., 3d bounding boxes or cad models), for which collection at scale is expensive and often intractable. to address this shortcoming. Holistic 3d scene understanding entails estimation of both layout configuration and object geometry in a 3d environment. recent works have shown advances in 3d scene estimation from various input modalities (e.g., images, 3d scans), by leveraging 3d supervision (e.g., 3d bounding boxes or cad models), for which collection at scale is ex pensive and often intractable. to address this. Implementation of cvpr'23: learning 3d scene priors with 2d supervision yinyunie scenepriors. in cvpr 2023. 3d scene generation. single view reconstruction. input. Instead, we rely on 2d supervision from multi view rgb images. our method represents a 3d scene as a latent vector, from which we can progressively decode to a sequence of objects characterized by their class categories, 3d bounding boxes, and meshes. with our trained autoregressive decoder representing the scene prior, our method facilitates.
Learning 3d Scene Priors With 2d Supervision Implementation of cvpr'23: learning 3d scene priors with 2d supervision yinyunie scenepriors. in cvpr 2023. 3d scene generation. single view reconstruction. input. Instead, we rely on 2d supervision from multi view rgb images. our method represents a 3d scene as a latent vector, from which we can progressively decode to a sequence of objects characterized by their class categories, 3d bounding boxes, and meshes. with our trained autoregressive decoder representing the scene prior, our method facilitates. Holistic 3d scene understanding entails estimation of both layout configuration and object geometry in a 3d environment. recent works have shown advances in 3d scene estimation from various input modalities (e.g., images, 3d scans), by leveraging 3d supervision (e.g., 3d bounding boxes or cad models), for which collection at scale is expensive and often intractable. Learning 3d scene priors with 2d supervision yinyu nie 1, angela dai , xiaoguang han2, matthias nießner1 1 technical university of munich 2 the chinese university of hong kong (shenzhen).
Comments are closed.