Decoupled and Interactive Regression Modeling for High-performance One-stage 3D Object Detection
- URL: http://arxiv.org/abs/2409.00690v1
- Date: Sun, 1 Sep 2024 10:47:22 GMT
- Title: Decoupled and Interactive Regression Modeling for High-performance One-stage 3D Object Detection
- Authors: Weiping Xiao, Yiqiang Wu, Chang Liu, Yu Qin, Xiaomao Li, Liming Xin,
- Abstract summary: Inadequate bounding box modeling in regression tasks constrains the performance of one-stage 3D object detection.
We propose Decoupled and Interactive Regression Modeling (DIRM) for one-stage detection.
- Score: 8.531052087985097
- License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
- Abstract: Inadequate bounding box modeling in regression tasks constrains the performance of one-stage 3D object detection. Our study reveals that the primary reason lies in two aspects: (1) The limited center-offset prediction seriously impairs the bounding box localization since many highest response positions significantly deviate from object centers. (2) The low-quality sample ignored in regression tasks significantly impacts the bounding box prediction since it produces unreliable quality (IoU) rectification. To tackle these problems, we propose Decoupled and Interactive Regression Modeling (DIRM) for one-stage detection. Specifically, Decoupled Attribute Regression (DAR) is implemented to facilitate long regression range modeling for the center attribute through an adaptive multi-sample assignment strategy that deeply decouples bounding box attributes. On the other hand, to enhance the reliability of IoU predictions for low-quality results, Interactive Quality Prediction (IQP) integrates the classification task, proficient in modeling negative samples, with quality prediction for joint optimization. Extensive experiments on Waymo and ONCE datasets demonstrate that DIRM significantly improves the performance of several state-of-the-art methods with minimal additional inference latency. Notably, DIRM achieves state-of-the-art detection performance on both the Waymo and ONCE datasets.
Related papers
- Distribution Discrepancy and Feature Heterogeneity for Active 3D Object Detection [18.285299184361598]
LiDAR-based 3D object detection is a critical technology for the development of autonomous driving and robotics.
We propose a novel and effective active learning (AL) method called Distribution Discrepancy and Feature Heterogeneity (DDFH)
It simultaneously considers geometric features and model embeddings, assessing information from both the instance-level and frame-level perspectives.
arXiv Detail & Related papers (2024-09-09T08:26:11Z) - Exploring Test-Time Adaptation for Object Detection in Continually Changing Environments [13.163784646113214]
Continual Test-Time Adaptation (CTTA) has recently emerged as a promising technique to gradually adapt a source-trained model to continually changing target domains.
We present AMROD, featuring three core components. Firstly, the object-level contrastive learning module extracts object-level features for contrastive learning to refine the feature representation in the target domain.
Secondly, the adaptive monitoring module dynamically skips unnecessary adaptation and updates the category-specific threshold based on predicted confidence scores to enable efficiency and improve the quality of pseudo-labels.
arXiv Detail & Related papers (2024-06-24T08:30:03Z) - Self-supervised Feature Adaptation for 3D Industrial Anomaly Detection [59.41026558455904]
We focus on multi-modal anomaly detection. Specifically, we investigate early multi-modal approaches that attempted to utilize models pre-trained on large-scale visual datasets.
We propose a Local-to-global Self-supervised Feature Adaptation (LSFA) method to finetune the adaptors and learn task-oriented representation toward anomaly detection.
arXiv Detail & Related papers (2024-01-06T07:30:41Z) - Introducing 3DCNN ResNets for ASD full-body kinematic assessment: a comparison with hand-crafted features [1.3499500088995464]
We propose a newly adapted 3DCNN ResNet from and compare it to widely used hand-crafted features for motor ASD assessment.
Specifically, we developed a virtual reality environment with multiple motor tasks and trained models using both approaches.
Results show the proposed model achieves a maximum accuracy of 85$pm$3%, outperforming state-of-the-art end-to-end models with short 1-to-3 minute samples.
arXiv Detail & Related papers (2023-11-24T14:56:36Z) - The effect of data augmentation and 3D-CNN depth on Alzheimer's Disease
detection [51.697248252191265]
This work summarizes and strictly observes best practices regarding data handling, experimental design, and model evaluation.
We focus on Alzheimer's Disease (AD) detection, which serves as a paradigmatic example of challenging problem in healthcare.
Within this framework, we train predictive 15 models, considering three different data augmentation strategies and five distinct 3D CNN architectures.
arXiv Detail & Related papers (2023-09-13T10:40:41Z) - Small Object Detection via Coarse-to-fine Proposal Generation and
Imitation Learning [52.06176253457522]
We propose a two-stage framework tailored for small object detection based on the Coarse-to-fine pipeline and Feature Imitation learning.
CFINet achieves state-of-the-art performance on the large-scale small object detection benchmarks, SODA-D and SODA-A.
arXiv Detail & Related papers (2023-08-18T13:13:09Z) - Rethinking IoU-based Optimization for Single-stage 3D Object Detection [103.83141677242871]
We propose a Rotation-Decoupled IoU (RDIoU) method that can mitigate the rotation-sensitivity issue.
Our RDIoU simplifies the complex interactions of regression parameters by decoupling the rotation variable as an independent term.
arXiv Detail & Related papers (2022-07-19T15:35:23Z) - Uncertainty-Aware Adaptation for Self-Supervised 3D Human Pose
Estimation [70.32536356351706]
We introduce MRP-Net that constitutes a common deep network backbone with two output heads subscribing to two diverse configurations.
We derive suitable measures to quantify prediction uncertainty at both pose and joint level.
We present a comprehensive evaluation of the proposed approach and demonstrate state-of-the-art performance on benchmark datasets.
arXiv Detail & Related papers (2022-03-29T07:14:58Z) - Distribution-Aware Single-Stage Models for Multi-Person 3D Pose
Estimation [29.430404703883084]
We present a novel Distribution-Aware Single-stage (DAS) model for tackling the challenging multi-person 3D pose estimation problem.
The proposed DAS model simultaneously localizes person positions and their corresponding body joints in the 3D camera space in a one-pass manner.
Comprehensive experiments on benchmarks CMU Panoptic and MuPoTS-3D demonstrate the superior efficiency of the proposed DAS model.
arXiv Detail & Related papers (2022-03-15T07:30:27Z) - Secrets of 3D Implicit Object Shape Reconstruction in the Wild [92.5554695397653]
Reconstructing high-fidelity 3D objects from sparse, partial observation is crucial for various applications in computer vision, robotics, and graphics.
Recent neural implicit modeling methods show promising results on synthetic or dense datasets.
But, they perform poorly on real-world data that is sparse and noisy.
This paper analyzes the root cause of such deficient performance of a popular neural implicit model.
arXiv Detail & Related papers (2021-01-18T03:24:48Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.