A Light Perspective for 3D Object Detection

• URL: http://arxiv.org/abs/2503.07133v1
• Date: Mon, 10 Mar 2025 10:03:23 GMT
• Title: A Light Perspective for 3D Object Detection
• Authors: Marcelo Eduardo Pederiva, José Mario De Martino, Alessandro Zimmer,
• Abstract summary: This paper introduces a novel approach that incorporates cutting-edge Deep Learning techniques into the feature extraction process.<n>Our model, NextBEV, surpasses established feature extractors like ResNet50 and MobileNetV3.<n>By fusing these lightweight proposals, we have enhanced the accuracy of the VoxelNet-based model by 2.93% and improved the F1-score of the PointPillar-based model by approximately 20%.
• Score: 46.23578780480946
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Comprehending the environment and accurately detecting objects in 3D space are essential for advancing autonomous vehicle technologies. Integrating Camera and LIDAR data has emerged as an effective approach for achieving high accuracy in 3D Object Detection models. However, existing methodologies often rely on heavy, traditional backbones that are computationally demanding. This paper introduces a novel approach that incorporates cutting-edge Deep Learning techniques into the feature extraction process, aiming to create more efficient models without compromising performance. Our model, NextBEV, surpasses established feature extractors like ResNet50 and MobileNetV2. On the KITTI 3D Monocular detection benchmark, NextBEV achieves an accuracy improvement of 2.39%, having less than 10% of the MobileNetV3 parameters. Moreover, we propose changes in LIDAR backbones that decreased the original inference time to 10 ms. Additionally, by fusing these lightweight proposals, we have enhanced the accuracy of the VoxelNet-based model by 2.93% and improved the F1-score of the PointPillar-based model by approximately 20%. Therefore, this work contributes to establishing lightweight and powerful models for individual or fusion techniques, making them more suitable for onboard implementations.

Related papers

• UAVTwin: Neural Digital Twins for UAVs using Gaussian Splatting [57.63613048492219]
  We present UAVTwin, a method for creating digital twins from real-world environments and facilitating data augmentation for training downstream models embedded in unmanned aerial vehicles (UAVs) This is achieved by integrating 3D Gaussian Splatting (3DGS) for reconstructing backgrounds along with controllable synthetic human models that display diverse appearances and actions in multiple poses.
  arXiv  Detail & Related papers  (2025-04-02T22:17:30Z)
• MonoDINO-DETR: Depth-Enhanced Monocular 3D Object Detection Using a Vision Foundation Model [2.0624236247076397]
  This study employs a Vision Transformer (ViT)-based foundation model as the backbone, which excels at capturing global features for depth estimation.<n>It integrates a detection transformer (DETR) architecture to improve both depth estimation and object detection performance in a one-stage manner.<n>The proposed model outperforms recent state-of-the-art methods, as demonstrated through evaluations on the KITTI 3D benchmark and a custom dataset collected from high-elevation racing environments.
  arXiv  Detail & Related papers  (2025-02-01T04:37:13Z)
• GOOD: General Optimization-based Fusion for 3D Object Detection via LiDAR-Camera Object Candidates [10.534984939225014]
  3D object detection serves as the core basis of the perception tasks in autonomous driving. Good is a general optimization-based fusion framework that can achieve satisfying detection without training additional models. Experiments on both nuScenes and KITTI datasets are carried out and the results show that GOOD outperforms by 9.1% on mAP score compared with PointPillars.
  arXiv  Detail & Related papers  (2023-03-17T07:05:04Z)
• PV-RCNN++: Point-Voxel Feature Set Abstraction With Local Vector Representation for 3D Object Detection [100.60209139039472]
  We propose the PointVoxel Region based Convolution Neural Networks (PVRCNNs) for accurate 3D detection from point clouds. Our proposed PV-RCNNs significantly outperform previous state-of-the-art 3D detection methods on both the Open dataset and the highly-competitive KITTI benchmark.
  arXiv  Detail & Related papers  (2021-01-31T14:51:49Z)
• SA-Det3D: Self-Attention Based Context-Aware 3D Object Detection [9.924083358178239]
  We propose two variants of self-attention for contextual modeling in 3D object detection. We first incorporate the pairwise self-attention mechanism into the current state-of-the-art BEV, voxel and point-based detectors. Next, we propose a self-attention variant that samples a subset of the most representative features by learning deformations over randomly sampled locations.
  arXiv  Detail & Related papers  (2021-01-07T18:30:32Z)
• Reinforced Axial Refinement Network for Monocular 3D Object Detection [160.34246529816085]
  Monocular 3D object detection aims to extract the 3D position and properties of objects from a 2D input image. Conventional approaches sample 3D bounding boxes from the space and infer the relationship between the target object and each of them, however, the probability of effective samples is relatively small in the 3D space. We propose to start with an initial prediction and refine it gradually towards the ground truth, with only one 3d parameter changed in each step. This requires designing a policy which gets a reward after several steps, and thus we adopt reinforcement learning to optimize it.
  arXiv  Detail & Related papers  (2020-08-31T17:10:48Z)
• InfoFocus: 3D Object Detection for Autonomous Driving with Dynamic Information Modeling [65.47126868838836]
  We propose a novel 3D object detection framework with dynamic information modeling. Coarse predictions are generated in the first stage via a voxel-based region proposal network. Experiments are conducted on the large-scale nuScenes 3D detection benchmark.
  arXiv  Detail & Related papers  (2020-07-16T18:27:08Z)
• PerMO: Perceiving More at Once from a Single Image for Autonomous Driving [76.35684439949094]
  We present a novel approach to detect, segment, and reconstruct complete textured 3D models of vehicles from a single image. Our approach combines the strengths of deep learning and the elegance of traditional techniques. We have integrated these algorithms with an autonomous driving system.
  arXiv  Detail & Related papers  (2020-07-16T05:02:45Z)
• Improving 3D Object Detection through Progressive Population Based Augmentation [91.56261177665762]
  We present the first attempt to automate the design of data augmentation policies for 3D object detection. We introduce the Progressive Population Based Augmentation (PPBA) algorithm, which learns to optimize augmentation strategies by narrowing down the search space and adopting the best parameters discovered in previous iterations. We find that PPBA may be up to 10x more data efficient than baseline 3D detection models without augmentation, highlighting that 3D detection models may achieve competitive accuracy with far fewer labeled examples.
  arXiv  Detail & Related papers  (2020-04-02T05:57:02Z)
• Generative Multi-Stream Architecture For American Sign Language Recognition [15.717424753251674]
  Training on datasets with low feature-richness for complex applications limit optimal convergence below human performance. We propose a generative multistream architecture, eliminating the need for additional hardware with the intent to improve feature convergence without risking impracticability. Our methods have achieved 95.62% validation accuracy with a variance of 1.42% from training, outperforming past models by 0.45% in validation accuracy and 5.53% in variance.
  arXiv  Detail & Related papers  (2020-03-09T21:04:51Z)

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.