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Found 8 papers, 1 papers with code
Multi-Instance Pose Networks: Rethinking Top-Down Pose Estimation
Specifically, we achieve 70. 0 AP on CrowdPose and 42. 5 AP on OCHuman test sets, a significant improvement of 2. 4 AP and 6. 5 AP over the prior art, respectively.
Ranked #1 on
Multi-Person Pose Estimation
on OCHuman
A Unified View-Graph Selection Framework for Structure from Motion
Accuracy and efficiency of large-scale SfM is crucially dependent on the input view-graph.
On Pairwise Costs for Network Flow Multi-Object Tracking
Multi-object tracking has been recently approached with the min-cost network flow optimization techniques.
Dynamic Body VSLAM with Semantic Constraints
We show results on the challenging KITTI urban dataset for accuracy of motion segmentation and reconstruction of the trajectory and shape of moving objects relative to ground truth.
View-graph Selection Framework for SfM
Accuracy and efficiency of large-scale SfM is crucially dependent on the input view-graph.
A Theory of Refractive Photo-Light-Path Triangulation
3D reconstruction of transparent refractive objects like a plastic bottle is challenging: they lack appearance related visual cues and merely reflect and refract light from the surrounding environment.
Learning to Generate Synthetic Data via Compositing
The synthesizer and target networks are trained in an adversarial manner wherein each network is updated with a goal to outdo the other.
Multi-modal 3D Human Pose Estimation with 2D Weak Supervision in Autonomous Driving
3D human pose estimation (HPE) in autonomous vehicles (AV) differs from other use cases in many factors, including the 3D resolution and range of data, absence of dense depth maps, failure modes for LiDAR, relative location between the camera and LiDAR, and a high bar for estimation accuracy.
