POD: Predictive Object Detection with Single-Frame FMCW LiDAR Point Cloud

• URL: http://arxiv.org/abs/2504.05649v1
• Date: Tue, 08 Apr 2025 03:53:28 GMT
• Title: POD: Predictive Object Detection with Single-Frame FMCW LiDAR Point Cloud
• Authors: Yining Shi, Kun Jiang, Xin Zhao, Kangan Qian, Chuchu Xie, Tuopu Wen, Mengmeng Yang, Diange Yang,
• Abstract summary: This paper explores the unique advantage of Frequency Modulated Continuous Wave (FMCW) LiDAR in autonomous perception.<n>Given a single frame FMCW point cloud with radial velocity measurements, we expect that our object detector can detect the short-term future locations of objects.<n>We propose a novel POD framework, the core idea of which is to generate a virtual future point using a ray casting mechanism.
• Score: 14.873096346810723
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: LiDAR-based 3D object detection is a fundamental task in the field of autonomous driving. This paper explores the unique advantage of Frequency Modulated Continuous Wave (FMCW) LiDAR in autonomous perception. Given a single frame FMCW point cloud with radial velocity measurements, we expect that our object detector can detect the short-term future locations of objects using only the current frame sensor data and demonstrate a fast ability to respond to intermediate danger. To achieve this, we extend the standard object detection task to a novel task named predictive object detection (POD), which aims to predict the short-term future location and dimensions of objects based solely on current observations. Typically, a motion prediction task requires historical sensor information to process the temporal contexts of each object, while our detector's avoidance of multi-frame historical information enables a much faster response time to potential dangers. The core advantage of FMCW LiDAR lies in the radial velocity associated with every reflected point. We propose a novel POD framework, the core idea of which is to generate a virtual future point using a ray casting mechanism, create virtual two-frame point clouds with the current and virtual future frames, and encode these two-frame voxel features with a sparse 4D encoder. Subsequently, the 4D voxel features are separated by temporal indices and remapped into two Bird's Eye View (BEV) features: one decoded for standard current frame object detection and the other for future predictive object detection. Extensive experiments on our in-house dataset demonstrate the state-of-the-art standard and predictive detection performance of the proposed POD framework.

Related papers

• Doracamom: Joint 3D Detection and Occupancy Prediction with Multi-view 4D Radars and Cameras for Omnidirectional Perception [9.76463525667238]
  We propose Doracamom, the first framework that fuses multi-view cameras and 4D radar for joint 3D object detection and semantic occupancy prediction.<n>Code and models will be publicly available.
  arXiv  Detail & Related papers  (2025-01-26T04:24:07Z)
• Future Does Matter: Boosting 3D Object Detection with Temporal Motion Estimation in Point Cloud Sequences [25.74000325019015]
  We introduce a novel LiDAR 3D object detection framework, namely LiSTM, to facilitate spatial-temporal feature learning with cross-frame motion forecasting information. We have conducted experiments on the aggregation and nuScenes datasets to demonstrate that the proposed framework achieves superior 3D detection performance.
  arXiv  Detail & Related papers  (2024-09-06T16:29:04Z)
• Learning Temporal Cues by Predicting Objects Move for Multi-camera 3D Object Detection [9.053936905556204]
  We propose a model called DAP (Detection After Prediction), consisting of a two-branch network. The features predicting the current objects from branch (i) is fused into branch (ii) to transfer predictive knowledge. Our model can be used plug-and-play, showing consistent performance gain.
  arXiv  Detail & Related papers  (2024-04-02T02:20:47Z)
• 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)
• DetZero: Rethinking Offboard 3D Object Detection with Long-term Sequential Point Clouds [55.755450273390004]
  Existing offboard 3D detectors always follow a modular pipeline design to take advantage of unlimited sequential point clouds. We have found that the full potential of offboard 3D detectors is not explored mainly due to two reasons: (1) the onboard multi-object tracker cannot generate sufficient complete object trajectories, and (2) the motion state of objects poses an inevitable challenge for the object-centric refining stage. To tackle these problems, we propose a novel paradigm of offboard 3D object detection, named DetZero.
  arXiv  Detail & Related papers  (2023-06-09T16:42:00Z)
• TrajectoryFormer: 3D Object Tracking Transformer with Predictive Trajectory Hypotheses [51.60422927416087]
  3D multi-object tracking (MOT) is vital for many applications including autonomous driving vehicles and service robots. We present TrajectoryFormer, a novel point-cloud-based 3D MOT framework.
  arXiv  Detail & Related papers  (2023-06-09T13:31:50Z)
• MoDAR: Using Motion Forecasting for 3D Object Detection in Point Cloud Sequences [38.7464958249103]
  We propose MoDAR, using motion forecasting outputs as a type of virtual modality, to augment LiDAR point clouds. A fused point cloud of both raw sensor points and the virtual points can then be fed to any off-the-shelf point-cloud based 3D object detector.
  arXiv  Detail & Related papers  (2023-06-05T19:28:19Z)
• Recurrent Vision Transformers for Object Detection with Event Cameras [62.27246562304705]
  We present Recurrent Vision Transformers (RVTs), a novel backbone for object detection with event cameras. RVTs can be trained from scratch to reach state-of-the-art performance on event-based object detection. Our study brings new insights into effective design choices that can be fruitful for research beyond event-based vision.
  arXiv  Detail & Related papers  (2022-12-11T20:28:59Z)
• MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object Detection [8.305942415868042]
  Most LiDAR sensors generate a sequence of point clouds in real-time. Recent studies have revealed that substantial performance improvement can be achieved by exploiting the context present in a sequence of point sets. We propose a novel 3D object detection architecture, which can encode point cloud sequences acquired by multiple successive scans.
  arXiv  Detail & Related papers  (2022-12-01T11:24:47Z)
• Fully Convolutional One-Stage 3D Object Detection on LiDAR Range Images [96.66271207089096]
  FCOS-LiDAR is a fully convolutional one-stage 3D object detector for LiDAR point clouds of autonomous driving scenes. We show that an RV-based 3D detector with standard 2D convolutions alone can achieve comparable performance to state-of-the-art BEV-based detectors.
  arXiv  Detail & Related papers  (2022-05-27T05:42:16Z)
• LiDAR-based Online 3D Video Object Detection with Graph-based Message Passing and Spatiotemporal Transformer Attention [100.52873557168637]
  3D object detectors usually focus on the single-frame detection, while ignoring the information in consecutive point cloud frames. In this paper, we propose an end-to-end online 3D video object detector that operates on point sequences.
  arXiv  Detail & Related papers  (2020-04-03T06:06:52Z)

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.