Titlebook: Computer Vision – ECCV 2024; 18th European Confer Ale? Leonardis,Elisa Ricci,Gül Varol Conference proceedings 2025 The Editor(s) (if applic

背帶

愛哭

,External Knowledge Enhanced 3D Scene Generation from?Sketch,cluding the 3D object instances as well as their layout. Experiments on the 3D-FRONT dataset show that our model improves FID, CKL by 17.41%, 37.18% in 3D scene generation and FID, KID by 19.12%, 20.06% in 3D scene completion compared to the nearest competitor DiffuScene.

avulsion

: Gradient Guided Generalizable Reconstruction,n with data-driven priors from fast feed-forward prediction methods. Experiments on urban-driving and drone datasets show that . generalizes across diverse large scenes and accelerates the reconstruction process by at least . while achieving comparable or better realism compared?to 3DGS, and also be

種類

,DreamScene360: Unconstrained Text-to-3D Scene Generation with?Panoramic Gaussian Splatting,ues inherent in single-view inputs, we impose semantic and geometric constraints on both synthesized and input camera views as regularizations. These guide the optimization of Gaussians, aiding in the reconstruction of unseen regions. In summary, our method offers a globally consistent 3D scene with

hypertension

sperse

Glycogen

https://doi.org/10.1007/3-540-30147-Xodel cross-window connections, and expand its receptive fields while maintaining linear complexity. We use SF-block as the main building block in a multi-scale U-shape network to form our Specformer. In addition, we introduce an uncertainty-driven loss function, which can reinforce the network’s att

GET

Reproduction: Blossoms, Fruits, Seeds produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks. MapTracker significantly outperforms existing methods on both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional and the new consistency-aware metrics, respe

fabricate

FECK

https://doi.org/10.1007/978-1-4939-6795-7n mechanism. Specifically, X-Former first bootstraps vision-language representation learning and multimodal-to-multimodal generative learning from two frozen vision encoders, i.e., CLIP-ViT (CL-based) and MAE-ViT (MIM-based). It further bootstraps vision-to-language generative learning from a frozen