47 citations · 94 across the 8 of their papers we have counts for
7 papers · 1 filter
Towards Fast and Scalable Normal Integration using Continuous Components
Francesco Milano, Jen Jen Chung, Lionel Ott +1
Surface normal integration is a fundamental problem in computer vision, dealing with the objective of reconstructing a surface from its corresponding normal map. Existing approache…
Discontinuity-aware Normal Integration for Generic Central Camera Models
Francesco Milano, Manuel López-Antequera, Naina Dhingra +2
Recovering a 3D surface from its surface normal map, a problem known as normal integration, is a key component for photometric shape reconstruction techniques such as shape-from-sh…
NeuSurfEmb: A Complete Pipeline for Dense Correspondence-based 6D Object Pose Estimation without CAD Models
Francesco Milano, Jen Jen Chung, Hermann Blum +2
State-of-the-art approaches for 6D object pose estimation assume the availability of CAD models and require the user to manually set up physically-based rendering (PBR) pipelines f…
ISAR: A Benchmark for Single- and Few-Shot Object Instance Segmentation and Re-Identification
Nicolas Gorlo, Kenneth Blomqvist, Francesco Milano +1
Most object-level mapping systems in use today make use of an upstream learned object instance segmentation model. If we want to teach them about a new object or segmentation class…
Panoptic Vision-Language Feature Fields
Haoran Chen, Kenneth Blomqvist, Francesco Milano +1
Recently, methods have been proposed for 3D open-vocabulary semantic segmentation. Such methods are able to segment scenes into arbitrary classes based on text descriptions provide…
Unsupervised Continual Semantic Adaptation through Neural Rendering
Zhizheng Liu, Francesco Milano, Jonas Frey +3
An increasing amount of applications rely on data-driven models that are deployed for perception tasks across a sequence of scenes. Due to the mismatch between training and deploym…
Primal-Dual Mesh Convolutional Neural Networks
Francesco Milano, Antonio Loquercio, Antoni Rosinol +2
Recent works in geometric deep learning have introduced neural networks that allow performing inference tasks on three-dimensional geometric data by defining convolution, and somet…