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PLUMENet: Efficient 3D Object Detection from Stereo Images
[article]
2021
arXiv
pre-print
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Specifically, we directly construct a pseudo LiDAR feature volume (PLUME) in 3D space, which is then used to solve both depth estimation and object detection tasks. ...
3D object detection is a key component of many robotic applications such as self-driving vehicles. ...
To overcome these issues, we propose an efficient stereo-based detector by replacing the disparity cost volume in image space with a novel pseudo LiDAR feature volume (PLUME) in 3D space. ...
arXiv:2101.06594v3
fatcat:spdwihruxney5omg23w54gszky
RoarNet: A Robust 3D Object Detection based on RegiOn Approximation Refinement
[article]
2018
arXiv
pre-print
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Inspired by PointNet, RoarNet_3D processes 3D point clouds directly without any loss of data, leading to precise detection. We evaluate our method in KITTI, a 3D object detection benchmark. ...
We present RoarNet, a new approach for 3D object detection from a 2D image and 3D Lidar point clouds. ...
ACKNOWLEDGMENT The work was in part supported by Berkeley Deep Drive. Kiwoo Shin is supported by Samsung Scholarship. ...
arXiv:1811.03818v1
fatcat:ftjcbkgihbea7aiv22aanscb7y
ESGN: Efficient Stereo Geometry Network for Fast 3D Object Detection
[article]
2022
arXiv
pre-print
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We hope that our efficient stereo geometry network can provide more possible directions for fast 3D object detection. Our source code will be released. ...
We argue that the main reason is due to the poor geometry-aware feature representation in 3D space. To solve this problem, we propose an efficient stereo geometry network (ESGN). ...
Prediction Heads 3D Detection Head Similar to [3, 5] , we perform 3D detection by a classification head and a regression head. ...
arXiv:2111.14055v2
fatcat:zculgzbobzakxkll4baabgmfmm
Complex-YOLO: Real-time 3D Object Detection on Point Clouds
[article]
2018
arXiv
pre-print
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Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency. ...
In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space ...
Conclusion In this paper we present the first real-time efficient deep learning model for 3D object detection on Lidar based point clouds. ...
arXiv:1803.06199v2
fatcat:ad7qzlclnvas7mtqs537khjqr4
Complex-YOLO: An Euler-Region-Proposal for Real-Time 3D Object Detection on Point Clouds
[chapter]
2019
Lecture Notes in Computer Science
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Our experiments on the KITTI benchmark suite show that we outperform current leading methods for 3D object detection specifically in terms of efficiency. ...
In this work, we describe a network that expands YOLOv2, a fast 2D standard object detector for RGB images, by a specific complex regression strategy to estimate multi-class 3D boxes in Cartesian space ...
Conclusion In this paper we present the first real-time efficient deep learning model for 3D object detection on Lidar based point clouds. ...
doi:10.1007/978-3-030-11009-3_11
fatcat:seo5lofl7fgabdmyupjuwatyzu
Mask2CAD: 3D Shape Prediction by Learning to Segment and Retrieve
[article]
2020
arXiv
pre-print
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We propose to leverage existing large-scale datasets of 3D models to understand the underlying 3D structure of objects seen in an image by constructing a CAD-based representation of the objects and their ...
We construct a joint embedding space between the detected regions of an image corresponding to an object and 3D CAD models, enabling retrieval of CAD models for an input RGB image. ...
By leveraging CAD models as shape representation, we are able to predict multiple distinct 3D objects per image efficiently (approximately 60ms per image). ...
arXiv:2007.13034v1
fatcat:yabpz6uoczdhtopk2fmmfkwqq4
F-Siamese Tracker: A Frustum-based Double Siamese Network for 3D Single Object Tracking
[article]
2020
arXiv
pre-print
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A main challenge in 3D single object tracking is how to reduce search space for generating appropriate 3D candidates. ...
For efficiency, our approach gains better performance with fewer candidates by reducing search space. ...
F-PointNet [10] uses 2D detection result to generate frustums in 3D space, which greatly reduces the search space. ...
arXiv:2010.11510v1
fatcat:a4na7ek475enzjll64biqucydi
RTS3D: Real-time Stereo 3D Detection from 4D Feature-Consistency Embedding Space for Autonomous Driving
[article]
2020
arXiv
pre-print
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In this paper, we propose an efficient and accurate 3D object detection method from stereo images, named RTS3D. ...
Although the recent image-based 3D object detection methods using Pseudo-LiDAR representation have shown great capabilities, a notable gap in efficiency and accuracy still exist compared with LiDAR-based ...
An image-based 3D object detection approach predicts the 3D box of objects more efficiently and accurately. 2.) ...
arXiv:2012.15072v1
fatcat:yp4v3vhcjvft5f6agkxydeuure
Shape Prior Non-Uniform Sampling Guided Real-time Stereo 3D Object Detection
[article]
2021
arXiv
pre-print
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FCE space splits the entire object region into 3D uniform grid latent space for feature sampling point generation, which ignores the importance of different object regions. ...
To solve these two issues, a recently introduced RTS3D builds an efficient 4D Feature-Consistency Embedding (FCE) space for the intermediate representation of object without depth supervision. ...
C. 3D detection As mentioned above, each 3D proposal is represented by the 4D feature in FCE space. Next, we use a 3D object detector to perform 3D detection. ...
arXiv:2106.10013v3
fatcat:sa4hbl7nufguxaqj46flhl4dje
RTM3D: Real-time Monocular 3D Detection from Object Keypoints for Autonomous Driving
[article]
2020
arXiv
pre-print
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In this work, we propose an efficient and accurate monocular 3D detection framework in single shot. ...
the dimension, location, and orientation in 3D space. ...
Figure 1 . 1 Overview of proposed method: We first predict ordinal keypoints projected in the image space by eight vertexes and a central point of a 3D object. ...
arXiv:2001.03343v1
fatcat:v34dca7k4vat3hp57yjfzt5d5y
3D Object Detection and Instance Segmentation from 3D Range and 2D Color Images
2021
Sensors
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We address those problems by proposing a novel object segmentation and detection system. First, we detect 2D objects based on RGB, depth only, or RGB-D images. ...
to perform the 3D instance segmentation and object detection. ...
Given RGB-D data or depth only data as input, 3D object detection aims to classify and localize objects in 3D space. ...
doi:10.3390/s21041213
pmid:33572289
fatcat:3rznt7afljg3lflvlcdqyccwgm
MoNet3D: Towards Accurate Monocular 3D Object Localization in Real Time
[article]
2020
arXiv
pre-print
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Monocular multi-object detection and localization in 3D space has been proven to be a challenging task. ...
Experiments on the KITTI dataset show that the accuracy for predicting the depth and horizontal coordinates of objects in 3D space can reach 96.25\% and 94.74\%, respectively. ...
Acknowledgements This work was supported by the National Natural Science Foundation of China under Contract 61971072. ...
arXiv:2006.16007v1
fatcat:icbn75qefjfxxl777ahs7vyf4e
Reinforced Axial Refinement Network for Monocular 3D Object Detection
[article]
2020
arXiv
pre-print
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in the 3D space. ...
Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. ...
The goal is to predict a tight bounding-box with a high 3D-IoU. Fig. 2 . The proposed framework for monocular 3D object detection. It is an iterative algorithm optimized by RL. ...
arXiv:2008.13748v1
fatcat:5eorvtrt3fgtzmyhetqkxmbr4a
Frustum VoxNet for 3D object detection from RGB-D or Depth images
[article]
2020
arXiv
pre-print
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In this work, we describe a new 3D object detection system from an RGB-D or depth-only point cloud. Our system first detects objects in 2D (either RGB or pseudo-RGB constructed from depth). ...
The next step is to detect 3D objects within the 3D frustums these 2D detections define. ...
inspired by 2D-driven 3D object detection approaches as in [10, 16] . ...
arXiv:1910.05483v2
fatcat:q4fdzf4albeb5n6j2i7xvsybyu
Frustum PointNets for 3D Object Detection from RGB-D Data
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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In this work, we study 3D object detection from RGB-D data in both indoor and outdoor scenes. ...
However, a key challenge of this approach is how to efficiently localize objects in point clouds of large-scale scenes (region proposal). ...
Towards this goal, we have to address one key challenge: how to efficiently propose possible locations of 3D objects in a 3D space. ...
doi:10.1109/cvpr.2018.00102
dblp:conf/cvpr/QiLWSG18
fatcat:za5o64qpcrgqvijmioux6clnpq
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