Paper Reading AI Learner

PointBeV: A Sparse Approach to BeV Predictions

2023-12-01 16:38:14
Loick Chambon, Eloi Zablocki, Mickael Chen, Florent Bartoccioni, Patrick Perez, Matthieu Cord

Abstract

Bird's-eye View (BeV) representations have emerged as the de-facto shared space in driving applications, offering a unified space for sensor data fusion and supporting various downstream tasks. However, conventional models use grids with fixed resolution and range and face computational inefficiencies due to the uniform allocation of resources across all cells. To address this, we propose PointBeV, a novel sparse BeV segmentation model operating on sparse BeV cells instead of dense grids. This approach offers precise control over memory usage, enabling the use of long temporal contexts and accommodating memory-constrained platforms. PointBeV employs an efficient two-pass strategy for training, enabling focused computation on regions of interest. At inference time, it can be used with various memory/performance trade-offs and flexibly adjusts to new specific use cases. PointBeV achieves state-of-the-art results on the nuScenes dataset for vehicle, pedestrian, and lane segmentation, showcasing superior performance in static and temporal settings despite being trained solely with sparse signals. We will release our code along with two new efficient modules used in the architecture: Sparse Feature Pulling, designed for the effective extraction of features from images to BeV, and Submanifold Attention, which enables efficient temporal modeling. Our code is available at this https URL.

Abstract (translated)

bird's-eye view (BeV) 表示已经成为了驾驶应用程序的事实共享空间,提供了一个统一的传感器数据融合空间,支持各种下游任务。然而,传统的模型使用固定的分辨率范围和网格,由于所有细胞资源均匀分配,导致计算效率低下。为了解决这个问题,我们提出了 PointBeV,一种新颖的稀疏 BeV 分割模型, operating on sparse BeV 细胞而不是密集网格。这种方法可以精确控制内存使用,使得可以使用长时上下文,并适应具有内存限制的平台。PointBeV 使用一种高效的两步策略进行训练,使得在感兴趣的区域进行集中计算。在推理时,它可以与各种内存/性能权衡灵活配合,并能够适应新的具体应用场景。PointBeV 在 nuScenes 数据集上取得了最先进的分数,在车辆、行人、车道分割中展示了在静态和时间设置下的卓越性能,尽管它仅使用稀疏信号进行训练。我们将发布我们的代码以及用于架构的两个新的高效模块:稀疏特征提取,用于从图像中有效提取特征到 BeV;子流注意,它允许有效的时序建模。我们的代码可在此处访问:https://www.aclweb.org/anthology/N22-11966

URL

https://arxiv.org/abs/2312.00703

PDF

https://arxiv.org/pdf/2312.00703.pdf


Tags
3D Action Action_Localization Action_Recognition Activity Adversarial Agent Attention Autonomous Bert Boundary_Detection Caption Chat Classification CNN Compressive_Sensing Contour Contrastive_Learning Deep_Learning Denoising Detection Dialog Diffusion Drone Dynamic_Memory_Network Edge_Detection Embedding Embodied Emotion Enhancement Face Face_Detection Face_Recognition Facial_Landmark Few-Shot Gait_Recognition GAN Gaze_Estimation Gesture Gradient_Descent Handwriting Human_Parsing Image_Caption Image_Classification Image_Compression Image_Enhancement Image_Generation Image_Matting Image_Retrieval Inference Inpainting Intelligent_Chip Knowledge Knowledge_Graph Language_Model Matching Medical Memory_Networks Multi_Modal Multi_Task NAS NMT Object_Detection Object_Tracking OCR Ontology Optical_Character Optical_Flow Optimization Person_Re-identification Point_Cloud Portrait_Generation Pose Pose_Estimation Prediction QA Quantitative Quantitative_Finance Quantization Re-identification Recognition Recommendation Reconstruction Regularization Reinforcement_Learning Relation Relation_Extraction Represenation Represenation_Learning Restoration Review RNN Salient Scene_Classification Scene_Generation Scene_Parsing Scene_Text Segmentation Self-Supervised Semantic_Instance_Segmentation Semantic_Segmentation Semi_Global Semi_Supervised Sence_graph Sentiment Sentiment_Classification Sketch SLAM Sparse Speech Speech_Recognition Style_Transfer Summarization Super_Resolution Surveillance Survey Text_Classification Text_Generation Tracking Transfer_Learning Transformer Unsupervised Video_Caption Video_Classification Video_Indexing Video_Prediction Video_Retrieval Visual_Relation VQA Weakly_Supervised Zero-Shot