We introduce a technique for detecting 3D objects and estimating their
position from a single image. Our method is built on top of a similar
state-of-the-art technique [1], but with improved accuracy. The approach
followed in this research first estimates common 3D properties of an object
using a Deep Convolutional Neural Network (DCNN), contrary to other frameworks
that only leverage centre-point predictions. We then combine these estimates
with geometric constraints provided by a 2D bounding box to produce a complete
3D bounding box. The first output of our network estimates the 3D object
orientation using a discrete-continuous loss [1]. The second output predicts
the 3D object dimensions with minimal variance. Here we also present our
extensions by augmenting light-weight feature extractors and a customized
multibin architecture. By combining these estimates with the geometric
constraints of the 2D bounding box, we can accurately (or comparatively)
determine the 3D object pose better than our baseline [1] on the KITTI 3D
detection benchmark [2].

通过改进的深度卷积神经网络以及多元箱子架构和几何限制，本研究在 KITTI 3D 检测基准测试上确定了更准确的三维对象姿态。

OriCon3D: 利用方向和置信度的有效 3D 物体检测

OriCon3D: Effective 3D Object Detection using Orientation and Confidence

Accurate 3D object detection in LiDAR based point clouds suffers from the
challenges of data sparsity and irregularities. Existing methods strive to
organize the points regularly, e.g. voxelize, pass them through a designed
2D/3D neural network, and then define object-level anchors that predict offsets
of 3D bounding boxes using collective evidences from all the points on the
objects of interest. Contrary to the state-of-the-art anchor-based methods,
based on the very nature of data sparsity, we observe that even points on an
individual object part are informative about semantic information of the
object. We thus argue in this paper for an approach opposite to existing
methods using object-level anchors. Inspired by compositional models, which
represent an object as parts and their spatial relations, we propose to
represent an object as composition of its interior non-empty voxels, termed
hotspots, and the spatial relations of hotspots. This gives rise to the
representation of Object as Hotspots (OHS). Based on OHS, we further propose an
anchor-free detection head with a novel ground truth assignment strategy that
deals with inter-object point-sparsity imbalance to prevent the network from
biasing towards objects with more points. Experimental results show that our
proposed method works remarkably well on objects with a small number of points.
Notably, our approach ranked 1st on KITTI 3D Detection Benchmark for cyclist
and pedestrian detection, and achieved state-of-the-art performance on NuScenes
3D Detection Benchmark.

采用热点及其空间关系的方式，提出适用于稀疏数据的 3D 物体检测方法，并在 KITTI 3D 检测基准测试中获得第一名，以及在 NuScenes 3D 检测基准测试中取得了最新的性能。