• Corpus ID: 236469032

Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth

  title={Aug3D-RPN: Improving Monocular 3D Object Detection by Synthetic Images with Virtual Depth},
  author={Chen-Hang He and Jianqiang Huang and Xiansheng Hua and Lei Zhang},
Current geometry-based monocular 3D object detection models can efficiently detect objects by leveraging perspective geometry, but their performance is limited due to the absence of accurate depth information. Though this issue can be alleviated in a depth-based model where a depth estimation module is plugged to predict depth information before 3D box reasoning, the introduction of such module dramatically reduces the detection speed. Instead of training a costly depth estimator, we propose a… 

Homography Loss for Monocular 3D Object Detection

A differentiable loss function, termed as Homography Loss, is proposed to achieve the goal, which exploits both 2D and 3D information, aiming at balancing the positional relationships between different objects by global constraints, so as to obtain more ac-curately predicted 3D boxes.

Omni3D: A Large Benchmark and Model for 3D Object Detection in the Wild

It is proved that O MNI 3D is a powerful dataset for 3D object recognition, show that it improves single-dataset performance and can accelerate learning on new smaller datasets via pre-training.

A Real-Time Distance Prediction via Deep Learning and Microsoft Kinect

A Kinect camera is used to extract the depth of image information and the depth information collected will be trained with deep learning architecture to perform a real-time distance prediction.



M3D-RPN: Monocular 3D Region Proposal Network for Object Detection

M3D-RPN is able to significantly improve the performance of both monocular 3D Object Detection and Bird's Eye View tasks within the KITTI urban autonomous driving dataset, while efficiently using a shared multi-class model.

Learning Depth-Guided Convolutions for Monocular 3D Object Detection

  • Mingyu DingYuqi Huo P. Luo
  • Computer Science
    2020 IEEE/CVF Conference on Computer Vision and Pattern Recognition Workshops (CVPRW)
  • 2020
D4LCN overcomes the limitation of conventional 2D convolutions and narrows the gap between image representation and 3D point cloud representation, where the filters and their receptive fields can be automatically learned from image-based depth maps.

MonoGRNet: A Geometric Reasoning Network for Monocular 3D Object Localization

This work proposes a novel IDE method that directly predicts the depth of the targeting 3D bounding box's center using sparse supervision, and demonstrates that MonoGRNet achieves state-of-the-art performance on challenging datasets.

Accurate Monocular 3D Object Detection via Color-Embedded 3D Reconstruction for Autonomous Driving

This paper proposes a monocular 3D object detection framework in the domain of autonomous driving, and proposes a multi-modal feature fusion module to embed the complementary RGB cue into the generated point clouds representation.

Monocular 3D Object Detection Leveraging Accurate Proposals and Shape Reconstruction

MonopolyPSR, a monocular 3D object detection method that leverages proposals and shape reconstruction, is presented and a novel projection alignment loss is devised to jointly optimize these tasks in the neural network to improve 3D localization accuracy.

SMOKE: Single-Stage Monocular 3D Object Detection via Keypoint Estimation

This paper argues that the 2D detection network is redundant and introduces non-negligible noise for 3D detection, and proposes a novel 3D object detection method, named SMOKE, in this paper that predicts a 3D bounding box for each detected object by combining a single keypoint estimate with regressed 3D variables.

Shift R-CNN: Deep Monocular 3D Object Detection With Closed-Form Geometric Constraints

The novel, geometrically constrained deep learning approach to monocular 3D object detection obtains top results on KITTI 3D Object Detection Benchmark, being the best among all monocular methods that do not use any pre-trained network for depth estimation.

Deep Fitting Degree Scoring Network for Monocular 3D Object Detection

A deep fitting degree scoring network for monocular 3D object detection, which aims to score fitting degree between proposals and object conclusively, and proposes FQNet, which can infer the 3D IoU between the3D proposals and the object solely based on 2D cues.

RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving

This work proposes an efficient and accurate monocular 3D detection framework in single shot that achieves state-of-the-art performance on the KITTI benchmark and predicts the nine perspective keypoints of a 3D bounding box in image space, and utilizes the geometric relationship of 3D and 2D perspectives.

MonoFENet: Monocular 3D Object Detection With Feature Enhancement Networks

The experimental results on the KITTI benchmark reveal that the MonoFENet method can achieve state-of-the-art performance for monocular 3D object detection.