Google Gemini, a multimodal AI by DeepMind, processes text, audio, images, and more. Gemini outperforms in AI benchmarks, is optimized for varied devices, and has been tested for safety and bias, adhering to responsible AI practices.
A One Stop 3D Target Reconstruction and multilevel Segmentation Method
We extend object tracking and 3D reconstruction algorithms to support continuous segmentation labels to leverage the advances in the 2D image segmentation, especially the Segment-Anything Model (SAM) which uses the pretrained neural network without additional training for new scenes, for 3D object segmentation.
GitHub Link
The GitHub link is https://github.com/ganlab/ostraIntroduce
OSTRA is an innovative 3D point cloud segmentation and reconstruction method called "One Stop 3D Target Reconstruction and Multilevel Segmentation Framework." It employs a Segment-Anything Model (SAM) for object segmentation and video object segmentation (VOS) to track segmented targets across frames. The process covers segmentation at various levels—semantic, instance, and part segmentation. The project provides tutorials, instructions for installation, and required models like SAM, DeAOT, XMem, and Grounding-DINO. A WebUI interface is available for easy access, and demos showcase complex object segmentation. The paper citation and acknowledgments are also included. We extend object tracking and 3D reconstruction algorithms to support continuous segmentation labels to leverage the advances in the 2D image segmentation, especially the Segment-Anything Model (SAM) which uses the pretrained neural network without additional training for new scenes, for 3D object segmentation.Content
OSTRA is a novel segmentation-then-reconstruction method for segmenting complex open objects in 3D point clouds. This method uses a Segment-Anything Model (SAM) to segment target objects and video object segmentation (VOS) technology to continuously track video frame segmentation targets. Our pipeline enables a complete segmentation process from videos to 3D cloud points and meshes in different level(semantic segmentation, instance segmentation and part segmentation). You can check our detailed tutorials here! This project is tested under python3.9, cuda11.5 and pytorch1.11.0. An equivalent or higher version is recommended. Our reconstruction process is based on Colmap. Please follow the instruction and install Colmap first. We developed WebUI that user can easily access. Two samples of complex object segmentation: Please considering cite our paper if you find this work useful! This work is based on Segment Anything, Track Anything, Segment and Track Anything, Colmap and Open3D.
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