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Found 5 papers, 3 papers with code
Mesh Strikes Back: Fast and Efficient Human Reconstruction from RGB videos
Human reconstruction and synthesis from monocular RGB videos is a challenging problem due to clothing, occlusion, texture discontinuities and sharpness, and framespecific pose changes.
ConceptFusion: Open-set Multimodal 3D Mapping
ConceptFusion leverages the open-set capabilities of today's foundation models pre-trained on internet-scale data to reason about concepts across modalities such as natural language, images, and audio.
gradSLAM: Automagically differentiable SLAM
Blending representation learning approaches with simultaneous localization and mapping (SLAM) systems is an open question, because of their highly modular and complex nature.
Geometric Consistency for Self-Supervised End-to-End Visual Odometry
We show that using a noisy teacher, which could be a standard VO pipeline, and by designing a loss term that enforces geometric consistency of the trajectory, we can train accurate deep models for VO that do not require ground-truth labels.
CalibNet: Geometrically Supervised Extrinsic Calibration using 3D Spatial Transformer Networks
During training, the network only takes as input a LiDAR point cloud, the corresponding monocular image, and the camera calibration matrix K. At train time, we do not impose direct supervision (i. e., we do not directly regress to the calibration parameters, for example).
