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Found 9 papers, 4 papers with code
Pose-Free Generalizable Rendering Transformer
To address this challenge, we introduce PF-GRT, a new Pose-Free framework for Generalizable Rendering Transformer, eliminating the need for pre-computed camera poses and instead leveraging feature-matching learned directly from data.
LEAP: Liberate Sparse-view 3D Modeling from Camera Poses
Are camera poses necessary for multi-view 3D modeling?
Doduo: Learning Dense Visual Correspondence from Unsupervised Semantic-Aware Flow
Incorporating semantic priors with self-supervised flow training, Doduo produces accurate dense correspondence robust to the dynamic changes of the scenes.
POPE: 6-DoF Promptable Pose Estimation of Any Object, in Any Scene, with One Reference
To mitigate this issue, we propose a general paradigm for object pose estimation, called Promptable Object Pose Estimation (POPE).
Few-View Object Reconstruction with Unknown Categories and Camera Poses
The reconstruction results under predicted poses are comparable to the ones using ground-truth poses.
DexMV: Imitation Learning for Dexterous Manipulation from Human Videos
While significant progress has been made on understanding hand-object interactions in computer vision, it is still very challenging for robots to perform complex dexterous manipulation.
Semi-Supervised 3D Hand-Object Poses Estimation with Interactions in Time
Estimating 3D hand and object pose from a single image is an extremely challenging problem: hands and objects are often self-occluded during interactions, and the 3D annotations are scarce as even humans cannot directly label the ground-truths from a single image perfectly.
Ranked #7 on
hand-object pose
on HO-3D
Hand-Object Contact Consistency Reasoning for Human Grasps Generation
Based on the hand-object contact consistency, we design novel objectives in training the human grasp generation model and also a new self-supervised task which allows the grasp generation network to be adjusted even during test time.
Robust Lane Detection from Continuous Driving Scenes Using Deep Neural Networks
Specifically, information of each frame is abstracted by a CNN block, and the CNN features of multiple continuous frames, holding the property of time-series, are then fed into the RNN block for feature learning and lane prediction.
