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

Showing 601625 of 1576 papers

TitleStatusHype
Group-Free 3D Object Detection via TransformersCode1
cMinMax: A Fast Algorithm to Find the Corners of an N-dimensional Convex PolytopeCode0
Semi-Supervised Domain Adaptation Using Target-Oriented Domain Augmentation for 3D Object DetectionCode0
Sense Less, Generate More: Pre-training LiDAR Perception with Masked Autoencoders for Ultra-Efficient 3D SensingCode0
Warp and Learn: Novel Views Generation for Vehicles and Other ObjectsCode0
Semi-supervised 3D Object Detection with PatchTeacher and PillarMixCode0
A*3D Dataset: Towards Autonomous Driving in Challenging EnvironmentsCode0
Exploring Diversity-based Active Learning for 3D Object Detection in Autonomous DrivingCode0
ScAR: Scaling Adversarial Robustness for LiDAR Object DetectionCode0
SCKD: Semi-Supervised Cross-Modality Knowledge Distillation for 4D Radar Object DetectionCode0
SE3D: A Framework For Saliency Method Evaluation In 3D ImagingCode0
SESS: Self-Ensembling Semi-Supervised 3D Object DetectionCode0
Class-balanced Grouping and Sampling for Point Cloud 3D Object DetectionCode0
SBNet: Sparse Blocks Network for Fast InferenceCode0
Explore the LiDAR-Camera Dynamic Adjustment Fusion for 3D Object DetectionCode0
Are Dense Labels Always Necessary for 3D Object Detection from Point Cloud?Code0
SaViD: Spectravista Aesthetic Vision Integration for Robust and Discerning 3D Object Detection in Challenging EnvironmentsCode0
SAILOR: Scaling Anchors via Insights into Latent Object RepresentationCode0
S^3-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer for Monocular 3D Object DetectionCode0
ePose: Let's Make EfficientPose More Generally ApplicableCode0
Entropy Loss: An Interpretability Amplifier of 3D Object Detection Network for Intelligent DrivingCode0
Resonant Scanning Design and Control for Fast Spatial SamplingCode0
RICCARDO: Radar Hit Prediction and Convolution for Camera-Radar 3D Object DetectionCode0
Redundant Queries in DETR-Based 3D Detection Methods: Unnecessary and PrunableCode0
Real-time 3D Traffic Cone Detection for Autonomous DrivingCode0
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Benchmark Results

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