MapAnything is an end-to-end trained Transformer model that can take multiple modalities as input and directly regress the decomposed metric 3D geometric structure of the scene. This model supports more than 12 different 3D reconstruction tasks, including multi-image SfM, multi-view stereo vision, and monocular metric depth estimation.

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STream3R is a scalable sequential 3D reconstruction model based on causal Transformer, which redefines point cloud map prediction as a decoder-only Transformer problem. It introduces a streaming processing framework, efficiently processes image sequences using causal attention, and can generalize well to various challenging scenarios, including dynamic scenes where traditional methods often fail.

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AnySplat is an advanced 3D Gaussian Splat rendering model that can efficiently generate high-quality 3D scenes from images taken from different perspectives. This model has fast inference capabilities and good generalization performance, providing an innovative solution for 3D reconstruction and rendering.

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AerialMegaDepth is a deep learning model focused on aerial-ground reconstruction and view synthesis, capable of reconstructing 3D scenes from aerial images and generating new viewpoints.

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DAS3R is a 3D model designed for static scene reconstruction, utilizing dynamic-aware Gaussian splatting technology to generate high-quality 3D reconstructions from images.

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SLAM3R is a real-time dense scene reconstruction system based on monocular RGB video, achieving 3D scene reconstruction through feedforward neural network point cloud regression.

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SLAM3R is a real-time dense scene reconstruction system based on monocular RGB video, capable of performing point cloud regression through feedforward neural networks to achieve dense 3D reconstruction.

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