背带
发表于 2025-3-23 10:33:57
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爱哭
发表于 2025-3-23 14:48:12
,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
发表于 2025-3-23 18:28:53
: 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
种类
发表于 2025-3-24 01:33:12
,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
发表于 2025-3-24 05:57:14
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sperse
发表于 2025-3-24 10:30:45
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Glycogen
发表于 2025-3-24 11:57:59
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
发表于 2025-3-24 17:00:11
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
发表于 2025-3-24 19:12:06
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FECK
发表于 2025-3-25 00:30:07
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