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Frustum

About: Frustum is a research topic. Over the lifetime, 1972 publications have been published within this topic receiving 11112 citations.


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TL;DR: Frustum ConvNet (F-ConvNet) as mentioned in this paper aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustumlevel features and supports an end-to-end and continuous estimation of oriented boxes in the 3D space.
Abstract: In this work, we propose a novel method termed \emph{Frustum ConvNet (F-ConvNet)} for amodal 3D object detection from point clouds. Given 2D region proposals in an RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. F-ConvNet aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustum-level features and supports an end-to-end and continuous estimation of oriented boxes in the 3D space. We also propose component variants of F-ConvNet, including an FCN variant that extracts multi-resolution frustum features, and a refined use of F-ConvNet over a reduced 3D space. Careful ablation studies verify the efficacy of these component variants. F-ConvNet assumes no prior knowledge of the working 3D environment and is thus dataset-agnostic. We present experiments on both the indoor SUN-RGBD and outdoor KITTI datasets. F-ConvNet outperforms all existing methods on SUN-RGBD, and at the time of submission it outperforms all published works on the KITTI benchmark. Code has been made available at: {\url{this https URL}.}

313 citations

Journal ArticleDOI
TL;DR: In this paper, thin-walled circular cylinders and truncated circular cones of aluminium alloy were subjected to axial static loading and their initial axial length and the outside diameter of cylinders and frusta were kept constant whilst their wall thickness was varied.

194 citations

Journal ArticleDOI
TL;DR: A highly parallel, linearly scalable technique of kd‐tree construction for ray tracing of dynamic geometry compatible with the high performing algorithms such as MLRTA or frustum tracing is presented.
Abstract: We present a highly parallel, linearly scalable technique of kd-tree construction for ray tracing of dynamic geometry. We use conventional kd-tree compatible with the high performing algorithms such as MLRTA or frustum tracing. Proposed technique offers exceptional construction speed maintaining reasonable kd-tree quality for rendering stage. The algorithm builds a kd-tree from scratch each frame, thus prior knowledge of motion/deformation or motion constraints are not required. We achieve nearly real-time performance of 7-12 FPS for models with 200K of dynamic triangles at 1024x1024 resolution with shadows and textures.

185 citations

Proceedings ArticleDOI
01 Nov 2019
TL;DR: A novel method termed Frustum ConvNet (F-ConvNet), which aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN).
Abstract: In this work, we propose a novel method termed Frustum ConvNet (F-ConvNet) for amodal 3D object detection from point clouds. Given 2D region proposals in an RGB image, our method first generates a sequence of frustums for each region proposal, and uses the obtained frustums to group local points. F-ConvNet aggregates point-wise features as frustum-level feature vectors, and arrays these feature vectors as a feature map for use of its subsequent component of fully convolutional network (FCN), which spatially fuses frustum-level features and supports an end-to-end and continuous estimation of oriented boxes in the 3D space. We also propose component variants of F-ConvNet, including an FCN variant that extracts multi-resolution frustum features, and a refined use of F-ConvNet over a reduced 3D space. Careful ablation studies verify the efficacy of these component variants. F-ConvNet assumes no prior knowledge of the working 3D environment and is thus dataset-agnostic. We present experiments on both the indoor SUN-RGBD and outdoor KITTI datasets. F-ConvNet outperforms all existing methods on SUN-RGBD, and at the time of submission it outperforms all published works on the KITTI benchmark. Code has been made available at: https://github.com/zhixinwang/frustum-convnet.

153 citations

Journal ArticleDOI
TL;DR: In this article, a simulator of machining geometric errors in five-axis machining by considering the effect of kinematic errors on the three-dimensional interference of the tool and the workpiece is presented.
Abstract: Kinematic errors due to geometric inaccuracies in five-axis machining centers cause deviations in tool positions and orientation from commanded values, which consequently affect geometric accuracy of the machined surface. As is well known in the machine tool industry, machining of a cone frustum as specified in NAS979 standard is a widely accepted final performance test for five-axis machining centers. A critical issue with this machining test is, however, that the influence of the machine's error sources on the geometric accuracy of the machined cone frustum is not fully understood by machine tool builders and thus it is difficult to find causes of machining errors. To address this issue, this paper presents a simulator of machining geometric errors in five-axis machining by considering the effect of kinematic errors on the three-dimensional interference of the tool and the workpiece. Kinematic errors of a five-axis machining center with tilting rotary table type are first identified by a DBB method. Using an error model of the machining center with identified kinematic errors and considering location and geometry of the workpiece, machining geometric error with respect to the nominal geometry of the workpiece is predicted and evaluated. In an aim to improve geometric accuracy of the machined surface, an error compensation for tool position and orientation is also presented. Finally, as an example, the machining of a cone frustum by using a straight end mill, as described in the standard NAS979, is considered in case studies to experimentally verify the prediction and the compensation of machining geometric errors in five-axis machining.

152 citations


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Performance
Metrics
No. of papers in the topic in previous years
YearPapers
202340
202291
202143
202065
201974
2018125