BEV

Published: 27 Jun 2022 Category: deep_learning

Papers

Multi-Camera 3D Object Detection

Lift, Splat, Shoot: Encoding Images From Arbitrary Camera Rigs by Implicitly Unprojecting to 3D

BEVDet: High-Performance Multi-Camera 3D Object Detection in Bird-Eye-View

BEVDet4D: Exploit Temporal Cues in Multi-camera 3D Object Detection

BEVerse: Unified Perception and Prediction in Birds-Eye-View for Vision-Centric Autonomous Driving

BEVFormer: Learning Bird’s-Eye-View Representation from Multi-Camera Images via Spatiotemporal Transformers

HFT: Lifting Perspective Representations via Hybrid Feature Transformation

M^2BEV: Multi-Camera Joint 3D Detection and Segmentation with Unified Birds-Eye View Representation

BEVFusion: Multi-Task Multi-Sensor Fusion with Unified Bird’s-Eye View Representation

BEVFusion: A Simple and Robust LiDAR-Camera Fusion Framework

A Simple Baseline for BEV Perception Without LiDAR

BEVDepth: Acquisition of Reliable Depth for Multi-view 3D Object Detection

PolarFormer: Multi-camera 3D Object Detection with Polar Transformers

ORA3D: Overlap Region Aware Multi-view 3D Object Detection

HD Map Construction

HDMapNet: An Online HD Map Construction and Evaluation Framework

VectorMapNet: End-to-end Vectorized HD Map Learning

UniFormer: Unified Multi-view Fusion Transformer for Spatial-Temporal Representation in Bird’s-Eye-View

Semantic Segmentation

LaRa: Latents and Rays for Multi-Camera Bird’s-Eye-View Semantic Segmentation

CoBEVT: Cooperative Bird’s Eye View Semantic Segmentation with Sparse Transformers