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Found 6 papers, 2 papers with code
ICGNet: A Unified Approach for Instance-Centric Grasping
Executing a successful grasp in a cluttered environment requires multiple levels of scene understanding: First, the robot needs to analyze the geometric properties of individual objects to find feasible grasps.
TULIP: Transformer for Upsampling of LiDAR Point Cloud
In this paper, we propose TULIP, a new method to reconstruct high-resolution LiDAR point clouds from low-resolution LiDAR input.
Lidar Line Selection with Spatially-Aware Shapley Value for Cost-Efficient Depth Completion
Lidar is a vital sensor for estimating the depth of a scene.
P3Depth: Monocular Depth Estimation with a Piecewise Planarity Prior
We focus on the supervised setup, in which ground-truth depth is available only at training time.
Ranked #6 on
Depth Estimation
on NYU-Depth V2
Don't Forget The Past: Recurrent Depth Estimation from Monocular Video
In particular, we put three different types of depth estimation (supervised depth prediction, self-supervised depth prediction, and self-supervised depth completion) into a common framework.
Self-supervised Object Motion and Depth Estimation from Video
We present a self-supervised learning framework to estimate the individual object motion and monocular depth from video.
