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 51–75 of 1576 papers

TitleStatusHype
GeminiFusion: Efficient Pixel-wise Multimodal Fusion for Vision TransformerCode2
Fully Test-Time Adaptation for Monocular 3D Object DetectionCode2
Drones Help Drones: A Collaborative Framework for Multi-Drone Object Trajectory Prediction and BeyondCode2
ViewFormer: Exploring Spatiotemporal Modeling for Multi-View 3D Occupancy Perception via View-Guided TransformersCode2
Commonsense Prototype for Outdoor Unsupervised 3D Object DetectionCode2
Scaling Multi-Camera 3D Object Detection through Weak-to-Strong ElicitingCode2
MonoCD: Monocular 3D Object Detection with Complementary DepthsCode2
HENet: Hybrid Encoding for End-to-end Multi-task 3D Perception from Multi-view CamerasCode2
NeRF-MAE: Masked AutoEncoders for Self-Supervised 3D Representation Learning for Neural Radiance FieldsCode2
SeaBird: Segmentation in Bird's View with Dice Loss Improves Monocular 3D Detection of Large ObjectsCode2
OV-Uni3DETR: Towards Unified Open-Vocabulary 3D Object Detection via Cycle-Modality PropagationCode2
Is Your LiDAR Placement Optimized for 3D Scene Understanding?Code2
RCooper: A Real-world Large-scale Dataset for Roadside Cooperative PerceptionCode2
MIM4D: Masked Modeling with Multi-View Video for Autonomous Driving Representation LearningCode2
LISO: Lidar-only Self-Supervised 3D Object DetectionCode2
SAFDNet: A Simple and Effective Network for Fully Sparse 3D Object DetectionCode2
EMIFF: Enhanced Multi-scale Image Feature Fusion for Vehicle-Infrastructure Cooperative 3D Object DetectionCode2
MultiCorrupt: A Multi-Modal Robustness Dataset and Benchmark of LiDAR-Camera Fusion for 3D Object DetectionCode2
Ray Denoising: Depth-aware Hard Negative Sampling for Multi-view 3D Object DetectionCode2
LiDAR-PTQ: Post-Training Quantization for Point Cloud 3D Object DetectionCode2
MixSup: Mixed-grained Supervision for Label-efficient LiDAR-based 3D Object DetectionCode2
RoboFusion: Towards Robust Multi-Modal 3D Object Detection via SAMCode2
WidthFormer: Toward Efficient Transformer-based BEV View TransformationCode2
OneFormer3D: One Transformer for Unified Point Cloud SegmentationCode2
FlashOcc: Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height PluginCode2
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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