MoNet: Motion-based Point Cloud Prediction Network

• URL: http://arxiv.org/abs/2011.10812v1
• Date: Sat, 21 Nov 2020 15:43:31 GMT
• Title: MoNet: Motion-based Point Cloud Prediction Network
• Authors: Fan Lu, Guang Chen, Yinlong Liu, Zhijun Li, Sanqing Qu, Tianpei Zou
• Abstract summary: 3D point clouds accurately model 3D information of surrounding environment. Due to point clouds are unordered and unstructured, point cloud prediction is challenging. We propose a novel motion-based neural network named MoNet to predict point clouds.
• Score: 13.336278321863595
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Predicting the future can significantly improve the safety of intelligent vehicles, which is a key component in autonomous driving. 3D point clouds accurately model 3D information of surrounding environment and are crucial for intelligent vehicles to perceive the scene. Therefore, prediction of 3D point clouds has great significance for intelligent vehicles, which can be utilized for numerous further applications. However, due to point clouds are unordered and unstructured, point cloud prediction is challenging and has not been deeply explored in current literature. In this paper, we propose a novel motion-based neural network named MoNet. The key idea of the proposed MoNet is to integrate motion features between two consecutive point clouds into the prediction pipeline. The introduction of motion features enables the model to more accurately capture the variations of motion information across frames and thus make better predictions for future motion. In addition, content features are introduced to model the spatial content of individual point clouds. A recurrent neural network named MotionRNN is proposed to capture the temporal correlations of both features. Besides, we propose an attention-based motion align module to address the problem of missing motion features in the inference pipeline. Extensive experiments on two large scale outdoor LiDAR datasets demonstrate the performance of the proposed MoNet. Moreover, we perform experiments on applications using the predicted point clouds and the results indicate the great application potential of the proposed method.

Related papers

• 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)
• OFMPNet: Deep End-to-End Model for Occupancy and Flow Prediction in Urban Environment [0.0]
  We introduce an end-to-end neural network methodology designed to predict the future behaviors of all dynamic objects in the environment. We propose a novel time-weighted motion flow loss, whose application has shown a substantial decrease in end-point error.
  arXiv  Detail & Related papers  (2024-04-02T19:37:58Z)
• TrajectoryNAS: A Neural Architecture Search for Trajectory Prediction [0.0]
  Trajectory prediction is a critical component of autonomous driving systems. This paper introduces TrajectoryNAS, a pioneering method that focuses on utilizing point cloud data for trajectory prediction.
  arXiv  Detail & Related papers  (2024-03-18T11:48:41Z)
• AGAR: Attention Graph-RNN for Adaptative Motion Prediction of Point Clouds of Deformable Objects [7.414594429329531]
  We propose an improved architecture for point cloud prediction of deformable 3D objects. Specifically, to handle deformable shapes, we propose a graph-based approach that learns and exploits the spatial structure of point clouds. The proposed adaptative module controls the composition of local and global motions for each point, enabling the network to model complex motions in deformable 3D objects more effectively.
  arXiv  Detail & Related papers  (2023-07-19T12:21:39Z)
• Deep Learning for Day Forecasts from Sparse Observations [60.041805328514876]
  Deep neural networks offer an alternative paradigm for modeling weather conditions. MetNet-3 learns from both dense and sparse data sensors and makes predictions up to 24 hours ahead for precipitation, wind, temperature and dew point. MetNet-3 has a high temporal and spatial resolution, respectively, up to 2 minutes and 1 km as well as a low operational latency.
  arXiv  Detail & Related papers  (2023-06-06T07:07:54Z)
• SLPC: a VRNN-based approach for stochastic lidar prediction and completion in autonomous driving [63.87272273293804]
  We propose a new LiDAR prediction framework that is based on generative models namely Variational Recurrent Neural Networks (VRNNs) Our algorithm is able to address the limitations of previous video prediction frameworks when dealing with sparse data by spatially inpainting the depth maps in the upcoming frames. We present a sparse version of VRNNs and an effective self-supervised training method that does not require any labels.
  arXiv  Detail & Related papers  (2021-02-19T11:56:44Z)
• IntentNet: Learning to Predict Intention from Raw Sensor Data [86.74403297781039]
  In this paper, we develop a one-stage detector and forecaster that exploits both 3D point clouds produced by a LiDAR sensor as well as dynamic maps of the environment. Our multi-task model achieves better accuracy than the respective separate modules while saving computation, which is critical to reducing reaction time in self-driving applications.
  arXiv  Detail & Related papers  (2021-01-20T00:31:52Z)
• PC-RGNN: Point Cloud Completion and Graph Neural Network for 3D Object Detection [57.49788100647103]
  LiDAR-based 3D object detection is an important task for autonomous driving. Current approaches suffer from sparse and partial point clouds of distant and occluded objects. In this paper, we propose a novel two-stage approach, namely PC-RGNN, dealing with such challenges by two specific solutions.
  arXiv  Detail & Related papers  (2020-12-18T18:06:43Z)
• 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)
• Pseudo-LiDAR Point Cloud Interpolation Based on 3D Motion Representation and Spatial Supervision [68.35777836993212]
  We propose a Pseudo-LiDAR point cloud network to generate temporally and spatially high-quality point cloud sequences. By exploiting the scene flow between point clouds, the proposed network is able to learn a more accurate representation of the 3D spatial motion relationship.
  arXiv  Detail & Related papers  (2020-06-20T03:11:04Z)
• MotionNet: Joint Perception and Motion Prediction for Autonomous Driving Based on Bird's Eye View Maps [34.24949016811546]
  We propose an efficient deep model, called MotionNet, to jointly perform perception and motion prediction from 3D point clouds. MotionNet takes a sequence of sweeps as input and outputs a bird's eye view (BEV) map, which encodes the object category and motion information in each grid cell.
  arXiv  Detail & Related papers  (2020-03-15T04:37:12Z)

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.