TimePillars: Temporally-Recurrent 3D LiDAR Object Detection

• URL: http://arxiv.org/abs/2312.17260v1
• Date: Fri, 22 Dec 2023 10:25:27 GMT
• Title: TimePillars: Temporally-Recurrent 3D LiDAR Object Detection
• Authors: Ernesto Lozano Calvo, Bernardo Taveira, Fredrik Kahl, Niklas Gustafsson, Jonathan Larsson, Adam Tonderski
• Abstract summary: TimePillars is a temporally-recurrent object detection pipeline. It exploits the pillar representation of LiDAR data across time. We show how basic building blocks are enough to achieve robust and efficient results.
• Score: 8.955064958311517
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection applied to LiDAR point clouds is a relevant task in robotics, and particularly in autonomous driving. Single frame methods, predominant in the field, exploit information from individual sensor scans. Recent approaches achieve good performance, at relatively low inference time. Nevertheless, given the inherent high sparsity of LiDAR data, these methods struggle in long-range detection (e.g. 200m) which we deem to be critical in achieving safe automation. Aggregating multiple scans not only leads to a denser point cloud representation, but it also brings time-awareness to the system, and provides information about how the environment is changing. Solutions of this kind, however, are often highly problem-specific, demand careful data processing, and tend not to fulfil runtime requirements. In this context we propose TimePillars, a temporally-recurrent object detection pipeline which leverages the pillar representation of LiDAR data across time, respecting hardware integration efficiency constraints, and exploiting the diversity and long-range information of the novel Zenseact Open Dataset (ZOD). Through experimentation, we prove the benefits of having recurrency, and show how basic building blocks are enough to achieve robust and efficient results.

Related papers

• StreamLTS: Query-based Temporal-Spatial LiDAR Fusion for Cooperative Object Detection [0.552480439325792]
  We propose Time-Aligned COoperative Object Detection (TA-COOD), for which we adapt widely used dataset OPV2V and DairV2X. Experiment results confirm the superior efficiency of our fully sparse framework compared to the state-of-the-art dense models.
  arXiv  Detail & Related papers  (2024-07-04T10:56:10Z)
• 3D Object Detection and High-Resolution Traffic Parameters Extraction Using Low-Resolution LiDAR Data [14.142956899468922]
  This study proposes an innovative framework that alleviates the need for multiple LiDAR systems and simplifies the laborious 3D annotation process. Using the 2D bounding box detection and extracted height information, this study is able to generate 3D bounding boxes automatically without human intervention.
  arXiv  Detail & Related papers  (2024-01-13T01:22:20Z)
• 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)
• Unsupervised Domain Adaptation for Self-Driving from Past Traversal Features [69.47588461101925]
  We propose a method to adapt 3D object detectors to new driving environments. Our approach enhances LiDAR-based detection models using spatial quantized historical features. Experiments on real-world datasets demonstrate significant improvements.
  arXiv  Detail & Related papers  (2023-09-21T15:00:31Z)
• SUIT: Learning Significance-guided Information for 3D Temporal Detection [15.237488449422008]
  We learn Significance-gUided Information for 3D Temporal detection (SUIT), which simplifies temporal information as sparse features for information fusion across frames. We evaluate our method on large-scale nuScenes and dataset, where our SUIT not only significantly reduces the memory and cost of temporal fusion, but also performs well over the state-of-the-art baselines.
  arXiv  Detail & Related papers  (2023-07-04T16:22:10Z)
• Generalized Few-Shot 3D Object Detection of LiDAR Point Cloud for Autonomous Driving [91.39625612027386]
  We propose a novel task, called generalized few-shot 3D object detection, where we have a large amount of training data for common (base) objects, but only a few data for rare (novel) classes. Specifically, we analyze in-depth differences between images and point clouds, and then present a practical principle for the few-shot setting in the 3D LiDAR dataset. To solve this task, we propose an incremental fine-tuning method to extend existing 3D detection models to recognize both common and rare objects.
  arXiv  Detail & Related papers  (2023-02-08T07:11:36Z)
• A Robust and Explainable Data-Driven Anomaly Detection Approach For Power Electronics [56.86150790999639]
  We present two anomaly detection and classification approaches, namely the Matrix Profile algorithm and anomaly transformer. The Matrix Profile algorithm is shown to be well suited as a generalizable approach for detecting real-time anomalies in streaming time-series data. A series of custom filters is created and added to the detector to tune its sensitivity, recall, and detection accuracy.
  arXiv  Detail & Related papers  (2022-09-23T06:09:35Z)
• Benchmarking the Robustness of LiDAR-Camera Fusion for 3D Object Detection [58.81316192862618]
  Two critical sensors for 3D perception in autonomous driving are the camera and the LiDAR. fusing these two modalities can significantly boost the performance of 3D perception models. We benchmark the state-of-the-art fusion methods for the first time.
  arXiv  Detail & Related papers  (2022-05-30T09:35:37Z)
• 3D-FCT: Simultaneous 3D Object Detection and Tracking Using Feature Correlation [0.0]
  3D-FCT is a Siamese network architecture that utilizes temporal information to simultaneously perform the related tasks of 3D object detection and tracking. Our proposed method is evaluated on the KITTI tracking dataset where it is shown to provide an improvement of 5.57% mAP over a state-of-the-art approach.
  arXiv  Detail & Related papers  (2021-10-06T06:36:29Z)
• StrObe: Streaming Object Detection from LiDAR Packets [73.27333924964306]
  Rolling shutter LiDARs emitted as a stream of packets, each covering a sector of the 360deg coverage. Modern perception algorithms wait for the full sweep to be built before processing the data, which introduces an additional latency. In this paper we propose StrObe, a novel approach that minimizes latency by ingesting LiDAR packets and emitting a stream of detections without waiting for the full sweep to be built.
  arXiv  Detail & Related papers  (2020-11-12T14:57:44Z)
• Streaming Object Detection for 3-D Point Clouds [29.465873948076766]
  LiDAR provides a prominent sensory modality that informs many existing perceptual systems. The latency for perceptual systems based on point cloud data can be dominated by the amount of time for a complete rotational scan. We show how operating on LiDAR data in its native streaming formulation offers several advantages for self driving object detection.
  arXiv  Detail & Related papers  (2020-05-04T21:55:15Z)

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.