Rethinking Point Cloud Filtering: A Non-Local Position Based Approach Unlike normal based techniques, our method does not require the normal information. The core idea is to first design a similarity metric to search the non-local similar patches of a queried local patch. We then map the non-...
In this paper, we propose a new filtering technique that decreases the number of comparisons between the query set and the search set to find highly similar documents. The proposed filtering technique utilizes Z-order prefix, based on the cosine similarity measure, in which only the most ...
Journal of Organizational and End User Computing Volume 34 • Issue 2 Text Similarity Measurement Method and Application of Online Medical Community Based on Density Peak Clustering Mingyang Li, School of Management, Jilin University, China Xinhua Bi, School of Management, Jilin University, China* ...
The methods based on the three-dimensional representation can be used in a complementary manner, since their performances depend on the type of forest under consideration [17,18]. There are other problems related to vegetation features that might be addressed in the 3D framework. They refer to ...
In both cases, the survey data (point clouds) were registered to a single digital surface model (DSM) in Leica Cyclone 8.0 software (Figure 2). The data were then filtered using the cloth simulation filtering (CSF) algorithm to classify and extract ground points [35]. CSF was used as a...
In contrast to previous real-time online approaches that process each incoming image in acquisition order, we show that applying a carefully selected order of (possibly a subset of) frames for pose estimation enables the performance of robust 3D reconstruction while automatically filtering out error-...
In contrast to previous real-time online approaches that process each incoming image in acquisition order, we show that applying a carefully selected order of (possibly a subset of) frames for pose estimation enables the performance of robust 3D reconstruction while automatically filtering out error-...
According to diverse theoretical methods, the existing single-frame detection methods can be roughly divided into the following several categories: methods based on background estimation filtering, local features, deep learning, principal component analysis, etc. The method based on background estimation ...
According to diverse theoretical methods, the existing single-frame detection methods can be roughly divided into the following several categories: methods based on background estimation filtering, local features, deep learning, principal component analysis, etc. The method based on background estimation ...
3.4.2. Sparsity of Supervision The KITTI DC dataset provides semi-dense ground-truth depth data for the training by accumulating a number of successive frames to the reference frame with outlier filtering. The density (i.e., precision) of the GT can vary depending on how many frames are acc...