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Found 6 papers, 0 papers with code
Ada3D : Exploiting the Spatial Redundancy with Adaptive Inference for Efficient 3D Object Detection
One reason for this high resource consumption is the presence of a large number of redundant background points in Lidar point clouds, resulting in spatial redundancy in both 3D voxel and dense BEV map representations.
Efficient Second-Order Plane Adjustment
Furthermore, as the optimal planes are functions of poses, this method actually ensures that the optimal planes for the current estimated poses can be obtained at each iteration, which benefits the convergence.
An Efficient Planar Bundle Adjustment Algorithm
In contrast, a depth sensor can record hundreds of points in a plane at a time, which results in a very large nonlinear least-squares problem even for a small-scale space.
Do not Omit Local Minimizer: a Complete Solution for Pose Estimation from 3D Correspondences
Besides, when the global minimizer is the solution, our algorithm achieves the same accuracy as previous algorithms that have guaranteed global optimality, but our algorithm is applicable to real-time applications.
Unsupervised Learning of Monocular Depth Estimation with Bundle Adjustment, Super-Resolution and Clip Loss
In this paper, we introduce a bundle adjustment framework and a super-resolution network to solve the above two problems.
