Convolutional neural network (CNN)-based deep learning (DL) has a wide variety of
applications in the geospatial and remote sensing (RS) sciences, and consequently has …

Object pose recovery has gained increasing attention in the computer vision field as it has
become an important problem in rapidly evolving technological areas related to autonomous …

In this paper, we aim at addressing two critical issues in the 3D detection task, including the
exploitation of multiple sensors (namely LiDAR point cloud and camera image), as well as …

Current 3D object detection methods are heavily influenced by 2D detectors. In order to
leverage architectures in 2D detectors, they often convert 3D point clouds to regular grids …

In this paper we propose to exploit multiple related tasks for accurate multi-sensor 3D object
detection. Towards this goal we present an end-to-end learnable architecture that reasons …

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 …

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 …

We present PointFusion, a generic 3D object detection method that leverages both image
and 3D point cloud information. Unlike existing methods that either use multi-stage pipelines …

Abstract 3D object detection has seen quick progress thanks to advances in deep learning
on point clouds. A few recent works have even shown state-of-the-art performance with just …

Recently, fusing the LiDAR point cloud and camera image to improve the performance and
robustness of 3D object detection has received more and more attention, as these two …