MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping
- URL: http://arxiv.org/abs/2403.15951v2
- Date: Sat, 12 Oct 2024 04:02:26 GMT
- Title: MapTracker: Tracking with Strided Memory Fusion for Consistent Vector HD Mapping
- Authors: Jiacheng Chen, Yuefan Wu, Jiaqi Tan, Hang Ma, Yasutaka Furukawa,
- Abstract summary: The paper presents a vector HD-mapping algorithm that formulates the mapping as a tracking task and uses a history of memory latents to ensure consistent reconstructions over time.
MapTracker significantly outperforms existing methods on both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional and the new consistency-aware metrics, respectively.
- Score: 21.5611219371754
- License:
- Abstract: This paper presents a vector HD-mapping algorithm that formulates the mapping as a tracking task and uses a history of memory latents to ensure consistent reconstructions over time. Our method, MapTracker, accumulates a sensor stream into memory buffers of two latent representations: 1) Raster latents in the bird's-eye-view (BEV) space and 2) Vector latents over the road elements (i.e., pedestrian-crossings, lane-dividers, and road-boundaries). The approach borrows the query propagation paradigm from the tracking literature that explicitly associates tracked road elements from the previous frame to the current, while fusing a subset of memory latents selected with distance strides to further enhance temporal consistency. A vector latent is decoded to reconstruct the geometry of a road element. The paper further makes benchmark contributions by 1) Improving processing code for existing datasets to produce consistent ground truth with temporal alignments and 2) Augmenting existing mAP metrics with consistency checks. MapTracker significantly outperforms existing methods on both nuScenes and Agroverse2 datasets by over 8% and 19% on the conventional and the new consistency-aware metrics, respectively. The code and models are available on our project page: https://map-tracker.github.io.
Related papers
- GenMapping: Unleashing the Potential of Inverse Perspective Mapping for Robust Online HD Map Construction [20.1127163541618]
We have designed a universal map generation framework, GenMapping.
The framework is established with a triadic synergy architecture, including principal and dual auxiliary branches.
A thorough array of experimental results shows that the proposed model surpasses current state-of-the-art methods in both semantic mapping and vectorized mapping, while also maintaining a rapid inference speed.
arXiv Detail & Related papers (2024-09-13T10:15:28Z) - DTCLMapper: Dual Temporal Consistent Learning for Vectorized HD Map Construction [20.6143278960295]
This paper focuses on temporal instance consistency and temporal map consistency learning.
DTCLMapper is a dual-stream temporal consistency learning module that combines instance embedding with geometry maps.
Experiments on well-recognized benchmarks indicate that the proposed DTCLMapper achieves state-of-the-art performance in vectorized mapping tasks.
arXiv Detail & Related papers (2024-05-09T02:58:55Z) - PTT: Point-Trajectory Transformer for Efficient Temporal 3D Object Detection [66.94819989912823]
We propose a point-trajectory transformer with long short-term memory for efficient temporal 3D object detection.
We use point clouds of current-frame objects and their historical trajectories as input to minimize the memory bank storage requirement.
We conduct extensive experiments on the large-scale dataset to demonstrate that our approach performs well against state-of-the-art methods.
arXiv Detail & Related papers (2023-12-13T18:59:13Z) - SparseTrack: Multi-Object Tracking by Performing Scene Decomposition
based on Pseudo-Depth [84.64121608109087]
We propose a pseudo-depth estimation method for obtaining the relative depth of targets from 2D images.
Secondly, we design a depth cascading matching (DCM) algorithm, which can use the obtained depth information to convert a dense target set into multiple sparse target subsets.
By integrating the pseudo-depth method and the DCM strategy into the data association process, we propose a new tracker, called SparseTrack.
arXiv Detail & Related papers (2023-06-08T14:36:10Z) - Tracking by Associating Clips [110.08925274049409]
In this paper, we investigate an alternative by treating object association as clip-wise matching.
Our new perspective views a single long video sequence as multiple short clips, and then the tracking is performed both within and between the clips.
The benefits of this new approach are two folds. First, our method is robust to tracking error accumulation or propagation, as the video chunking allows bypassing the interrupted frames.
Second, the multiple frame information is aggregated during the clip-wise matching, resulting in a more accurate long-range track association than the current frame-wise matching.
arXiv Detail & Related papers (2022-12-20T10:33:17Z) - Learning Dynamic Compact Memory Embedding for Deformable Visual Object
Tracking [82.34356879078955]
We propose a compact memory embedding to enhance the discrimination of the segmentation-based deformable visual tracking method.
Our method outperforms the excellent segmentation-based trackers, i.e., D3S and SiamMask on DAVIS 2017 benchmark.
arXiv Detail & Related papers (2021-11-23T03:07:12Z) - Multi-Object Tracking and Segmentation with a Space-Time Memory Network [12.043574473965318]
We propose a method for multi-object tracking and segmentation based on a novel memory-based mechanism to associate tracklets.
The proposed tracker, MeNToS, addresses particularly the long-term data association problem.
arXiv Detail & Related papers (2021-10-21T17:13:17Z) - Learning Spatio-Appearance Memory Network for High-Performance Visual
Tracking [79.80401607146987]
Existing object tracking usually learns a bounding-box based template to match visual targets across frames, which cannot accurately learn a pixel-wise representation.
This paper presents a novel segmentation-based tracking architecture, which is equipped with a local-temporal memory network to learn accurate-temporal correspondence.
arXiv Detail & Related papers (2020-09-21T08:12:02Z) - A SLAM Map Restoration Algorithm Based on Submaps and an Undirected
Connected Graph [0.0]
We present a method of reconstructing a complete global map of UAV datasets by sequentially merging the submaps.
Results show that the integrity of the mapping was significantly better than that of the current mainstream SLAM method.
arXiv Detail & Related papers (2020-07-29T04:26:36Z) - Road Network Metric Learning for Estimated Time of Arrival [93.0759529610483]
In this paper, we propose the Road Network Metric Learning framework for Estimated Time of Arrival (ETA)
It consists of two components: (1) a main regression task to predict the travel time, and (2) an auxiliary metric learning task to improve the quality of link embedding vectors.
We show that our method outperforms the state-of-the-art model and the promotion concentrates on the cold links with few data.
arXiv Detail & Related papers (2020-06-24T04:45:14Z)
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.