Search Results for author:
Found 7 papers, 3 papers with code
Let It Flow: Simultaneous Optimization of 3D Flow and Object Clustering
We identified the structural constraints and the use of large and strict rigid clusters as the main pitfall of the current approaches and we propose a novel clustering approach that allows for combination of overlapping soft clusters as well as non-overlapping rigid clusters representation.
Regularizing Self-supervised 3D Scene Flows with Surface Awareness and Cyclic Consistency
Learning without supervision how to predict 3D scene flows from point clouds is essential to many perception systems.
T-UDA: Temporal Unsupervised Domain Adaptation in Sequential Point Clouds
Deep perception models have to reliably cope with an open-world setting of domain shifts induced by different geographic regions, sensor properties, mounting positions, and several other reasons.
Similarity among the 2D-shapes and the analysis of dissimilarity scores
We present a conceptually simple and intuitive method to calculate and to measure the dissimilarities among 2D shapes.
Teachers in concordance for pseudo-labeling of 3D sequential data
We propose to leverage sequences of point clouds to boost the pseudolabeling technique in a teacher-student setup via training multiple teachers, each with access to different temporal information.
Learning for Active 3D Mapping
We propose an active 3D mapping method for depth sensors, which allow individual control of depth-measuring rays, such as the newly emerging solid-state lidars.
Controlling Robot Morphology from Incomplete Measurements
Mobile robots with complex morphology are essential for traversing rough terrains in Urban Search & Rescue missions (USAR).
