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 551–575 of 1576 papers

TitleStatusHype
Shape Anchor Guided Holistic Indoor Scene UnderstandingCode0
SPOT: Scalable 3D Pre-training via Occupancy Prediction for Learning Transferable 3D Representations—0
MEDL-U: Uncertainty-aware 3D Automatic Annotation based on Evidential Deep LearningCode0
Mutual Information-calibrated Conformal Feature Fusion for Uncertainty-Aware Multimodal 3D Object Detection at the Edge—0
Object2Scene: Putting Objects in Context for Open-Vocabulary 3D Detection—0
Semantics-aware LiDAR-Only Pseudo Point Cloud Generation for 3D Object Detection—0
SupFusion: Supervised LiDAR-Camera Fusion for 3D Object DetectionCode1
Polygon Intersection-over-Union Loss for Viewpoint-Agnostic Monocular 3D Vehicle Detection—0
SCP: Scene Completion Pre-training for 3D Object Detection—0
FusionFormer: A Multi-sensory Fusion in Bird's-Eye-View and Temporal Consistent Transformer for 3D Object Detection—0
Poster: Making Edge-assisted LiDAR Perceptions Robust to Lossy Point Cloud Compression—0
Weakly Supervised Point Clouds Transformer for 3D Object Detection—0
ClusterFusion: Leveraging Radar Spatial Features for Radar-Camera 3D Object Detection in Autonomous Vehicles—0
Diffusion-based 3D Object Detection with Random Boxes—0
Adv3D: Generating 3D Adversarial Examples for 3D Object Detection in Driving Scenarios with NeRF—0
Snow Removal for LiDAR Point Clouds with Spatio-temporal Conditional Random FieldsCode0
S^3-MonoDETR: Supervised Shape&Scale-perceptive Deformable Transformer for Monocular 3D Object DetectionCode0
MS23D: A 3D Object Detection Method Using Multi-Scale Semantic Feature Points to Construct 3D Feature Layer—0
Ego-Motion Estimation and Dynamic Motion Separation from 3D Point Clouds for Accumulating Data and Improving 3D Object Detection—0
3D Adversarial Augmentations for Robust Out-of-Domain Predictions—0
Group Regression for Query Based Object Detection and Tracking—0
SOGDet: Semantic-Occupancy Guided Multi-view 3D Object DetectionCode1
I3DOD: Towards Incremental 3D Object Detection via Prompting—0
Perspective-aware Convolution for Monocular 3D Object DetectionCode0
On Offline Evaluation of 3D Object Detection for Autonomous Driving—0
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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