3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds

• URL: http://arxiv.org/abs/2211.00746v1
• Date: Tue, 1 Nov 2022 20:59:38 GMT
• Title: 3DMODT: Attention-Guided Affinities for Joint Detection & Tracking in 3D Point Clouds
• Authors: Jyoti Kini, Ajmal Mian, Mubarak Shah
• Abstract summary: We propose a method for joint detection and tracking of multiple objects in 3D point clouds. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network.
• Score: 95.54285993019843
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We propose a method for joint detection and tracking of multiple objects in 3D point clouds, a task conventionally treated as a two-step process comprising object detection followed by data association. Our method embeds both steps into a single end-to-end trainable network eliminating the dependency on external object detectors. Our model exploits temporal information employing multiple frames to detect objects and track them in a single network, thereby making it a utilitarian formulation for real-world scenarios. Computing affinity matrix by employing features similarity across consecutive point cloud scans forms an integral part of visual tracking. We propose an attention-based refinement module to refine the affinity matrix by suppressing erroneous correspondences. The module is designed to capture the global context in affinity matrix by employing self-attention within each affinity matrix and cross-attention across a pair of affinity matrices. Unlike competing approaches, our network does not require complex post-processing algorithms, and processes raw LiDAR frames to directly output tracking results. We demonstrate the effectiveness of our method on the three tracking benchmarks: JRDB, Waymo, and KITTI. Experimental evaluations indicate the ability of our model to generalize well across datasets.

Related papers

• ADA-Track: End-to-End Multi-Camera 3D Multi-Object Tracking with Alternating Detection and Association [15.161640917854363]
  We introduce ADA-Track, a novel end-to-end framework for 3D MOT from multi-view cameras. We introduce a learnable data association module based on edge-augmented cross-attention. We integrate this association module into the decoder layer of a DETR-based 3D detector.
  arXiv  Detail & Related papers  (2024-05-14T19:02:33Z)
• PoIFusion: Multi-Modal 3D Object Detection via Fusion at Points of Interest [65.48057241587398]
  PoIFusion is a framework to fuse information of RGB images and LiDAR point clouds at the points of interest (PoIs) Our approach maintains the view of each modality and obtains multi-modal features by computation-friendly projection and computation. We conducted extensive experiments on nuScenes and Argoverse2 datasets to evaluate our approach.
  arXiv  Detail & Related papers  (2024-03-14T09:28:12Z)
• SeMoLi: What Moves Together Belongs Together [51.72754014130369]
  We tackle semi-supervised object detection based on motion cues. Recent results suggest that motion-based clustering methods can be used to pseudo-label instances of moving objects. We re-think this approach and suggest that both, object detection, as well as motion-inspired pseudo-labeling, can be tackled in a data-driven manner.
  arXiv  Detail & Related papers  (2024-02-29T18:54:53Z)
• Modeling Continuous Motion for 3D Point Cloud Object Tracking [54.48716096286417]
  This paper presents a novel approach that views each tracklet as a continuous stream. At each timestamp, only the current frame is fed into the network to interact with multi-frame historical features stored in a memory bank. To enhance the utilization of multi-frame features for robust tracking, a contrastive sequence enhancement strategy is proposed.
  arXiv  Detail & Related papers  (2023-03-14T02:58:27Z)
• Simultaneous Multiple Object Detection and Pose Estimation using 3D Model Infusion with Monocular Vision [21.710141497071373]
  Multiple object detection and pose estimation are vital computer vision tasks. We propose simultaneous neural modeling of both using monocular vision and 3D model infusion. Our Simultaneous Multiple Object detection and Pose Estimation network (SMOPE-Net) is an end-to-end trainable multitasking network.
  arXiv  Detail & Related papers  (2022-11-21T05:18:56Z)
• Ret3D: Rethinking Object Relations for Efficient 3D Object Detection in Driving Scenes [82.4186966781934]
  We introduce a simple, efficient, and effective two-stage detector, termed as Ret3D. At the core of Ret3D is the utilization of novel intra-frame and inter-frame relation modules. With negligible extra overhead, Ret3D achieves the state-of-the-art performance.
  arXiv  Detail & Related papers  (2022-08-18T03:48:58Z)
• M3DeTR: Multi-representation, Multi-scale, Mutual-relation 3D Object Detection with Transformers [78.48081972698888]
  We present M3DeTR, which combines different point cloud representations with different feature scales based on multi-scale feature pyramids. M3DeTR is the first approach that unifies multiple point cloud representations, feature scales, as well as models mutual relationships between point clouds simultaneously using transformers.
  arXiv  Detail & Related papers  (2021-04-24T06:48:23Z)
• A two-stage data association approach for 3D Multi-object Tracking [0.0]
  We adapt a two-stage dataassociation method which was successful in image-based tracking to the 3D setting. Our method outperforms the baseline using one-stagebipartie matching for data association by achieving 0.587 AMOTA in NuScenes validation set.
  arXiv  Detail & Related papers  (2021-01-21T15:50:17Z)
• Relation3DMOT: Exploiting Deep Affinity for 3D Multi-Object Tracking from View Aggregation [8.854112907350624]
  3D multi-object tracking plays a vital role in autonomous navigation. Many approaches detect objects in 2D RGB sequences for tracking, which is lack of reliability when localizing objects in 3D space. We propose a novel convolutional operation, named RelationConv, to better exploit the correlation between each pair of objects in the adjacent frames.
  arXiv  Detail & Related papers  (2020-11-25T16:14:40Z)
• Graph Neural Networks for 3D Multi-Object Tracking [28.121708602059048]
  3D Multi-object tracking (MOT) is crucial to autonomous systems. Recent work often uses a tracking-by-detection pipeline. We propose a novel feature interaction mechanism by introducing Graph Neural Networks.
  arXiv  Detail & Related papers  (2020-08-20T17:55:41Z)

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.