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 801–850 of 1576 papers

TitleStatusHype
Clusterformer: Cluster-based Transformer for 3D Object Detection in Point Clouds—0
A Simple Vision Transformer for Weakly Semi-supervised 3D Object Detection—0
CoIn: Contrastive Instance Feature Mining for Outdoor 3D Object Detection with Very Limited AnnotationsCode1
FocalFormer3D: Focusing on Hard Instance for 3D Object DetectionCode2
ObjectFusion: Multi-modal 3D Object Detection with Object-Centric Fusion—0
AShapeFormer: Semantics-Guided Object-Level Active Shape Encoding for 3D Object Detection via TransformersCode0
Implicit Surface Contrastive Clustering for LiDAR Point Clouds—0
Semi-Supervised Stereo-Based 3D Object Detection via Cross-View Consensus—0
Deep Dive Into Gradients: Better Optimization for 3D Object Detection With Gradient-Corrected IoU SupervisionCode1
X3KD: Knowledge Distillation Across Modalities, Tasks and Stages for Multi-Camera 3D Object Detection—0
Benchmarking Robustness of 3D Object Detection to Common CorruptionsCode1
Distilling Focal Knowledge From Imperfect Expert for 3D Object DetectionCode0
LiDAR-in-the-Loop Hyperparameter Optimization—0
TiG-BEV: Multi-view BEV 3D Object Detection via Target Inner-Geometry LearningCode1
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
A Comprehensive Study of the Robustness for LiDAR-based 3D Object Detectors against Adversarial AttacksCode1
Learning Object-level Point Augmentor for Semi-supervised 3D Object DetectionCode1
Learning for Vehicle-to-Vehicle Cooperative Perception under Lossy CommunicationCode1
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
MAELi: Masked Autoencoder for Large-Scale LiDAR Point Clouds—0
ConQueR: Query Contrast Voxel-DETR for 3D Object DetectionCode1
VINet: Lightweight, Scalable, and Heterogeneous Cooperative Perception for 3D Object Detection—0
MegaPose: 6D Pose Estimation of Novel Objects via Render & CompareCode2
BEV-MAE: Bird's Eye View Masked Autoencoders for Point Cloud Pre-training in Autonomous Driving ScenariosCode1
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
Towards Accurate Ground Plane Normal Estimation from Ego-MotionCode1
SSDA3D: Semi-supervised Domain Adaptation for 3D Object Detection from Point CloudCode1
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
MGTANet: Encoding Sequential LiDAR Points Using Long Short-Term Motion-Guided Temporal Attention for 3D Object DetectionCode1
BEV-LGKD: A Unified LiDAR-Guided Knowledge Distillation Framework for BEV 3D Object DetectionCode1
Attention-Based Depth Distillation with 3D-Aware Positional Encoding for Monocular 3D Object DetectionCode0
BEVUDA: Multi-geometric Space Alignments for Domain Adaptive BEV 3D Object Detection—0
Superpoint Transformer for 3D Scene Instance SegmentationCode1
3DPPE: 3D Point Positional Encoding for Multi-Camera 3D Object Detection TransformersCode1
3D Dual-Fusion: Dual-Domain Dual-Query Camera-LiDAR Fusion for 3D Object DetectionCode1
Sparse2Dense: Learning to Densify 3D Features for 3D Object DetectionCode1
UpCycling: Semi-supervised 3D Object Detection without Sharing Raw-level Unlabeled Scenes—0
Transformation-Equivariant 3D Object Detection for Autonomous Driving—0
AeDet: Azimuth-invariant Multi-view 3D Object DetectionCode1
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world LearningCode2
Context-Aware Data Augmentation for LIDAR 3D Object Detection—0
Sparse4D: Multi-view 3D Object Detection with Sparse Spatial-Temporal FusionCode2
BEVFormer v2: Adapting Modern Image Backbones to Bird's-Eye-View Recognition via Perspective SupervisionCode4
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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