SOTAVerified

3D Object Detection

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.

( Image credit: AVOD )

Papers

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Showing 1101–1150 of 1576 papers

TitleStatusHype
Eloss in the way: A Sensitive Input Quality Metrics for Intelligent DrivingCode0
AOP-Net: All-in-One Perception Network for Joint LiDAR-based 3D Object Detection and Panoptic Segmentation—0
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks—0
PTA-Det: Point Transformer Associating Point cloud and Image for 3D Object Detection—0
OA-BEV: Bringing Object Awareness to Bird's-Eye-View Representation for Multi-Camera 3D Object Detection—0
Object Detection in 3D Point Clouds via Local Correlation-Aware Point Embedding—0
Hierarchical Point Attention for Indoor 3D Object Detection—0
Super Sparse 3D Object Detection—0
MonoEdge: Monocular 3D Object Detection Using Local Perspectives—0
StereoDistill: Pick the Cream from LiDAR for Distilling Stereo-based 3D Object Detection—0
Implicit Surface Contrastive Clustering for LiDAR Point Clouds—0
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds—0
MetaBEV: Solving Sensor Failures for 3D Detection and Map Segmentation—0
Semi-Supervised Stereo-Based 3D Object Detection via Cross-View Consensus—0
X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection—0
Distilling Focal Knowledge From Imperfect Expert for 3D Object DetectionCode0
Towards Universal LiDAR-Based 3D Object Detection by Multi-Domain Knowledge Transfer—0
ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion—0
Query Refinement Transformer for 3D Instance Segmentation—0
A Simple Vision Transformer for Weakly Semi-supervised 3D Object Detection—0
AShapeFormer: Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via TransformersCode0
LiDAR-in-the-Loop Hyperparameter Optimization—0
Monocular 3D Object Detection using Multi-Stage Approaches with Attention and Slicing aided hyper inference—0
OBMO: One Bounding Box Multiple Objects for Monocular 3D Object DetectionCode0
Multi-level and multi-modal feature fusion for accurate 3D object detection in Connected and Automated Vehicles—0
DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention—0
VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection—0
MAELi: Masked Autoencoder for Large-Scale LiDAR Point Clouds—0
Focal-PETR: Embracing Foreground for Efficient Multi-Camera 3D Object Detection—0
Multi-Sem Fusion: Multimodal Semantic Fusion for 3D Object Detection—0
SemanticBEVFusion: Rethink LiDAR-Camera Fusion in Unified Bird's-Eye View Representation for 3D Object Detection—0
DA-CIL: Towards Domain Adaptive Class-Incremental 3D Object Detection—0
3D Object Aided Self-Supervised Monocular Depth Estimation—0
IDMS: Instance Depth for Multi-scale Monocular 3D Object Detection—0
BEV-SAN: Accurate BEV 3D Object Detection via Slice Attention Networks—0
BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection—0
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes—0
Transformation-Equivariant 3D Object Detection for Autonomous Driving—0
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
You Only Label Once: 3D Box Adaptation from Point Cloud to Image via Semi-Supervised Learning—0
Multi-Camera Multi-Object Tracking on the Move via Single-Stage Global Association Approach—0
ImLiDAR: Cross-Sensor Dynamic Message Propagation Network for 3D Object Detection—0
PAI3D: Painting Adaptive Instance-Prior for 3D Object Detection—0
Towards 3D Object Detection with 2D Supervision—0
Recursive Cross-View: Use Only 2D Detectors to Achieve 3D Object Detection without 3D Annotations—0
Boosting Semi-Supervised 3D Object Detection with Semi-SamplingCode0
Hyperbolic Cosine Transformer for LiDAR 3D Object Detection—0
3D Harmonic Loss: Towards Task-consistent and Time-friendly 3D Object Detection on Edge for V2X OrchestrationCode0
Ground Plane Matters: Picking Up Ground Plane Prior in Monocular 3D Object Detection—0
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All datasetsnuScenesScanNetV2SUN-RGBD valKITTI Cars EasyKITTI Cars HardnuScenes Camera OnlyKITTI Cars Easy valKITTI Cars Moderate valnuscenes Camera-RadarView-of-Delft (val)KITTI Cars Hard valDAIR-V2X-I

Benchmark Results

#ModelMetricClaimedVerifiedStatus
1EA-LSSNDS0.78—Unverified
2MMFusion-eNDS0.77—Unverified
3MegFusionNDS0.77—Unverified
4RacoonPowerNDS0.76—Unverified
5BEVFusion-eNDS0.76—Unverified
6DeepInteraction-largeNDS0.76—Unverified
7DeepInteraction-eNDS0.76—Unverified
8DAANDS0.75—Unverified
9FusionVPENDS0.75—Unverified
10CenterPoint-FusionNDS0.75—Unverified