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3D Object Detection
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3D Object Detection is a task in computer vision where the goal is to identify and locate objects in a 3D environment based on their shape, location, and orientation. It involves detecting the presence of objects and determining their location in the 3D space in real-time. This task is crucial for applications such as autonomous vehicles, robotics, and augmented reality.
<span style="color:grey; opacity: 0.6">( Image credit: AVOD )</span>
논문
이 태스크와 하위 분야의 관련 논문입니다. 실제 성능 비교는 평가 결과에서 확인하세요.
Beyond One Shot, Beyond One Perspective: Cross-View and Long-Horizon Distillation for Better LiDAR Representations
MambaFusion: Height-Fidelity Dense Global Fusion for Multi-modal 3D Object Detection
A Survey of Multi-sensor Fusion Perception for Embodied AI: Background, Methods, Challenges and Prospects
Teleoperated Driving: a New Challenge for 3D Object Detection in Compressed Point Clouds
Vision-based Lifting of 2D Object Detections for Automated Driving
DySS: Dynamic Queries and State-Space Learning for Efficient 3D Object Detection from Multi-Camera Videos
Gaussian2Scene: 3D Scene Representation Learning via Self-supervised Learning with 3D Gaussian Splatting
Cosmos-Drive-Dreams: Scalable Synthetic Driving Data Generation with World Foundation Models
SpatialLM: Training Large Language Models for Structured Indoor Modeling
SpikeSMOKE: Spiking Neural Networks for Monocular 3D Object Detection with Cross-Scale Gated Coding
FALO: Fast and Accurate LiDAR 3D Object Detection on Resource-Constrained Devices
Simulate Any Radar: Attribute-Controllable Radar Simulation via Waveform Parameter Embedding
Rooms from Motion: Un-posed Indoor 3D Object Detection as Localization and Mapping
The Meeseeks Mesh: Spatially Consistent 3D Adversarial Objects for BEV Detector
RQR3D: Reparametrizing the regression targets for BEV-based 3D object detection
CodeMerge: Codebook-Guided Model Merging for Robust Test-Time Adaptation in Autonomous Driving
InstanceBEV: Unifying Instance and BEV Representation for Global Modeling
MoRAL: Motion-aware Multi-Frame 4D Radar and LiDAR Fusion for Robust 3D Object Detection
DepthFusion: Depth-Aware Hybrid Feature Fusion for LiDAR-Camera 3D Object Detection
PillarMamba: Learning Local-Global Context for Roadside Point Cloud via Hybrid State Space Model
MonoCoP: Chain-of-Prediction for Monocular 3D Object Detection
DualDiff: Dual-branch Diffusion Model for Autonomous Driving with Semantic Fusion
HeAL3D: Heuristical-enhanced Active Learning for 3D Object Detection
Efficient On-Chip Implementation of 4D Radar-Based 3D Object Detection on Hailo-8L
Floating Car Observers in Intelligent Transportation Systems: Detection Modeling and Temporal Insights
A Multimodal Hybrid Late-Cascade Fusion Network for Enhanced 3D Object Detection
A Review of 3D Object Detection with Vision-Language Models
Lightweight LiDAR-Camera 3D Dynamic Object Detection and Multi-Class Trajectory Prediction
RoPETR: Improving Temporal Camera-Only 3D Detection by Integrating Enhanced Rotary Position Embedding
Self-Supervised Pre-training with Combined Datasets for 3D Perception in Autonomous Driving
Weak Cube R-CNN: Weakly Supervised 3D Detection using only 2D Bounding Boxes
GATE3D: Generalized Attention-based Task-synergized Estimation in 3D*
RICCARDO: Radar Hit Prediction and Convolution for Camera-Radar 3D Object Detection
Detect Anything 3D in the Wild
PRIMEDrive-CoT: A Precognitive Chain-of-Thought Framework for Uncertainty-Aware Object Interaction in Driving Scene Scenario
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