455 research outputs found
Frustum PointNets for 3D Object Detection from RGB-D Data
In this work, we study 3D object detection from RGB-D data in both indoor and
outdoor scenes. While previous methods focus on images or 3D voxels, often
obscuring natural 3D patterns and invariances of 3D data, we directly operate
on raw point clouds by popping up RGB-D scans. However, a key challenge of this
approach is how to efficiently localize objects in point clouds of large-scale
scenes (region proposal). Instead of solely relying on 3D proposals, our method
leverages both mature 2D object detectors and advanced 3D deep learning for
object localization, achieving efficiency as well as high recall for even small
objects. Benefited from learning directly in raw point clouds, our method is
also able to precisely estimate 3D bounding boxes even under strong occlusion
or with very sparse points. Evaluated on KITTI and SUN RGB-D 3D detection
benchmarks, our method outperforms the state of the art by remarkable margins
while having real-time capability.Comment: 15 pages, 12 figures, 14 table
3D Object Detection Using Scale Invariant and Feature Reweighting Networks
3D object detection plays an important role in a large number of real-world
applications. It requires us to estimate the localizations and the orientations
of 3D objects in real scenes. In this paper, we present a new network
architecture which focuses on utilizing the front view images and frustum point
clouds to generate 3D detection results. On the one hand, a PointSIFT module is
utilized to improve the performance of 3D segmentation. It can capture the
information from different orientations in space and the robustness to
different scale shapes. On the other hand, our network obtains the useful
features and suppresses the features with less information by a SENet module.
This module reweights channel features and estimates the 3D bounding boxes more
effectively. Our method is evaluated on both KITTI dataset for outdoor scenes
and SUN-RGBD dataset for indoor scenes. The experimental results illustrate
that our method achieves better performance than the state-of-the-art methods
especially when point clouds are highly sparse.Comment: The Thirty-Third AAAI Conference on Artificial Intelligence (AAAI-19
- …