This limitation significantly restricts the potential applications of NeRF in autonomous driving contexts, such as scene editing, high-precision map construction, and related functionalities. To overcome these challenges, we propose a novel NeRF-based approach tailored to address the complexities associated...
SyncNoise-NeRF-Edit SyncNoise-GS-Edit SyncNoise-CosXL Citation If our work is useful for your research, please consider citing: @article{li2024syncnoise, title={SyncNoise: Geometrically Consistent Noise Prediction for Text-based 3D Scene Editing}, author={Li, Ruihuang and Chen, Liyi and Zhang...
为了解决单目胃镜局部区域视图稀疏导致的性能下降问题,我们将预重建点云中的几何先验纳入 NeRF 的训练中,这为预先捕获的观察视图和生成的未观察视图引入了新的基于几何的损失。与其他最近的 NeRF 方法相比,我们的方法在质量和数量上都展示了胃内新视角的高保真图像渲染。 NeRF + 大场景/自动驾驶 NeRF On-the-go: ...
(2022). GLIDE: Towards photorealistic image generation and editing with text-guided diffusion models. In Proceedings of the 39th international conference on machine learning, PMLR (Vol. 162). Ohta, Y., Kanade, T., & Sakai, T. (1978). An analysis system for scenes containing objects with ...
The strategy of NeRF to reconstruct a 3D scene is to not directly use the explicit scene set shape to represent the light distribution of the scene and to directly synthesize the image in the new perspective only based on the positional internal reference and the image. First, the 3D ...
(2021) decompose a scene into objects using Slot Attention and condition a NeRF-based decoder on a latent code to vary object shape and appearance. Their model does encode object position and rotation implicitly and does not provide an explicit interpretable 3D parametrization like our method. ...
Scene Editing: READ can move and remove the cars in different views. A panorama with larger view can be synthesized by changing the camera parameters. Scene Stitching: READ is able to synthesize the larger driving scenes and update local areas with obvious changes in road conditions. Novel ...
Scene Editing Image diffusion models can be used for image inpainting without explicit training on the task [53, 63]. We leverage this property to edit scenes in 3D by re- sampling a region in the 3D coarse latent c. Specifically, at each denoising step, ...
Customize yourNeRF: Adaptive Source Driven 3D Scene Editing via Local-Global Iterative Training Pytorch implementation ofCustomize your NeRF: Adaptive Source Driven 3D Scene Editing viaLocal-Global Iterative Training Runze He*, Shaofei Huang*, Xuecheng Nie, Tianrui Hui, Luoqi Liu, Jiao Dai, Jizhong...
DreamEditor utilizes the text encoder of a pretrained text-to-Image diffusion model to automatically identify the regions to be edited based on the semantics of the text prompts. Subsequently, DreamEditor optimizes the editing region and aligns its geometry and texture with the text prompts through...