Deep Auxiliary Learning for Point Cloud Generation
نویسندگان
چکیده
منابع مشابه
Learning Efficient Point Cloud Generation for Dense 3D Object Reconstruction
Conventional methods of 3D object generative modeling learn volumetric predictions using deep networks with 3D convolutional operations, which are direct analogies to classical 2D ones. However, these methods are computationally wasteful in attempt to predict 3D shapes, where information is rich only on the surfaces. In this paper, we propose a novel 3D generative modeling framework to efficien...
متن کاملDeep Learning for Cloud Detection
The SPOT 6-7 satellite ground segment includes a systematic and automatic cloud detection step in order to feed a catalogue with a binary cloud mask and an appropriate confidence measure. However, current approaches for cloud detection, that are mostly based on machine learning and hand crafted features, have shown lack of robustness. In other tasks such as image recognition, deep learning meth...
متن کاملDeep Auxiliary Learning for Visual Localization and Odometry
Localization is an indispensable component of a robot’s autonomy stack that enables it to determine where it is in the environment, essentially making it a precursor for any action execution or planning. Although convolutional neural networks have shown promising results for visual localization, they are still grossly outperformed by state-of-the-art local feature-based techniques. In this work...
متن کاملInteractive Learning for Point-Cloud Motion Segmentation
Segmenting a moving foreground (fg) from its background (bg) is a fundamental step in many Machine Vision and Computer Graphics applications. Nevertheless, hardly any attempts have been made to tackle this problem in dynamic 3D scanned scenes. Scanned dynamic scenes are typically challenging due to noise and large missing parts. Here, we present a novel approach for motion segmentation in dynam...
متن کاملLearning 3D Point Cloud Histograms
In this paper we show how using histograms based on the angular relationships between a subset of point normals in a 3D point Cloud can be used in a machine learning algorithm in order to recognize different classes of objects given by their 3D point clouds. This approach extends the work done by Gary Bradski at Willow Garage on point clouds recognition by applying a machine learning approach t...
متن کاملذخیره در منابع من
با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید
ژورنال
عنوان ژورنال: IEEE Access
سال: 2020
ISSN: 2169-3536
DOI: 10.1109/access.2020.2968489