Nov 22, 2017 · 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.

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, ...

We propose a novel detection pipeline that combines both mature 2D object detectors and the state-of-the-art 3D deep learning techniques. In our pipeline, we ...

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, ...

Jun 30, 2024 · This network performs 2D OD on the RGB image first. Then the 2D Bounding Box (2D BB) is utilized as a form a region proposals in the 3D space.

Our frustum PointNet predicts a (oriented and amodal) 3D bounding box for the object from the points in frustum.

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.

This document provides additional technical details, ex- tra analysis experiments, more quantitative results and qual- itative test results to the main ...

This work directly operates on raw point clouds by popping up RGBD scans and leverages both mature 2D object detectors and advanced 3D deep learning for ...

Sep 25, 2018 · Frustum Pointnet is a novel framework for RGB-D data based object detection. Instead of solely relying on 3D proposals, this method leverages ...