AI & ML Papers
Photo
🔥 Geometric Context Transformer for Streaming 3D Reconstruction
📅 Published on Apr 15
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2604.14141
• PDF: https://arxiv.org/pdf/2604.14141
• Project Page: https://technology.robbyant.com/lingbot-map
🤖 Models citing this paper:
• https://huggingface.co/robbyant/lingbot-map
• https://huggingface.co/agramoi/lingbot-map
• https://huggingface.co/maujim/lingbot-map-long-only
🚀 Spaces citing this paper:
• https://huggingface.co/spaces/limonsyrah/lingbot-3d
• https://huggingface.co/spaces/mohan007/lingbot-3d
• https://huggingface.co/spaces/Fifthoply/lingbot-3d-ZERO
━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus
#GeometricDeepLearning #3DReconstructionAlgorithms #StreamingComputerVision #TransformerArchitectures #PointCloudProcessing
💡 The paper presents a new approach to streaming 3D reconstruction, which involves recovering 3D information such as camera poses and point clouds from a video stream. This task requires geometric accuracy, temporal consistency, and computational efficiency. To address this problem, the authors introduce LingBot-Map, a feed-forward 3D foundation model that uses a geometric context transformer architecture. The key component of this architecture is a specialized attention mechanism that integrates three main elements: an anchor context, a pose-reference window, and a trajectory memory. These elements work together to address coordinate grounding, dense geometric cues, and long-range drift correction, allowing the model to maintain a compact streaming state while retaining rich geometric context. The result is a model that can perform stable and efficient inference at around 20 frames per second on high-resolution inputs, even over long sequences exceeding 10,000 frames. The authors evaluate their approach on various benchmarks and demonstrate that it outperforms both existing streaming and iterative optimization-based methods, achieving superior performance in terms of geometric accuracy and temporal consistency. Overall, the paper contributes a novel and effective approach to streaming 3D reconstruction, with potential applications in areas such as robotics, computer vision, and virtual reality.
📅 Published on Apr 15
🔗 Links:
• GitHub: https://github.com/huggingface
• arXiv: https://arxiv.org/abs/2604.14141
• PDF: https://arxiv.org/pdf/2604.14141
• Project Page: https://technology.robbyant.com/lingbot-map
🤖 Models citing this paper:
• https://huggingface.co/robbyant/lingbot-map
• https://huggingface.co/agramoi/lingbot-map
• https://huggingface.co/maujim/lingbot-map-long-only
🚀 Spaces citing this paper:
• https://huggingface.co/spaces/limonsyrah/lingbot-3d
• https://huggingface.co/spaces/mohan007/lingbot-3d
• https://huggingface.co/spaces/Fifthoply/lingbot-3d-ZERO
━━━━━━━━━━━━━━━━━━━━━━━━
📢 By: https://xn--r1a.website/PaperNexus
#GeometricDeepLearning #3DReconstructionAlgorithms #StreamingComputerVision #TransformerArchitectures #PointCloudProcessing
GitHub
Hugging Face
The AI community building the future. Hugging Face has 443 repositories available. Follow their code on GitHub.