DINO-VO achieves state-of-the-art monocular visual odometry accuracy and generalization by training a differentiable patch selector together with multi-task features and inverse-depth bundle adjustment.
Plgslam: Progressive neural scene represenation with local to global bundle adjustment
2 Pith papers cite this work. Polarity classification is still indexing.
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GaussianDWM uses 3D Gaussians with embedded linguistic features, language-guided sampling, and dual-condition generation for unified scene understanding and multi-modal output in driving world models.
citing papers explorer
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DINO-VO: Learning Where to Focus for Enhanced State Estimation
DINO-VO achieves state-of-the-art monocular visual odometry accuracy and generalization by training a differentiable patch selector together with multi-task features and inverse-depth bundle adjustment.
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GaussianDWM: 3D Gaussian Driving World Model for Unified Scene Understanding and Multi-Modal Generation
GaussianDWM uses 3D Gaussians with embedded linguistic features, language-guided sampling, and dual-condition generation for unified scene understanding and multi-modal output in driving world models.