Papers 3D Semantic Occupancy Prediction
“3D Semantic Occupancy Prediction” 태그가 달린 논문 47편 · 필터 해제
FMOcc: TPV-Driven Flow Matching for 3D Occupancy Prediction with Selective State Space Model
3D semantic occupancy prediction plays a pivotal role in autonomous driving. However, inherent limitations of fewframe images and redundancy in 3D space compromise prediction accuracy for occluded and distant scenes. Exi…
3D Semantic Occupancy PredictionAutonomous DrivingPredictionOut-of-Distribution Semantic Occupancy Prediction
3D Semantic Occupancy Prediction is crucial for autonomous driving, providing a dense, semantically rich environmental representation. However, existing methods focus on in-distribution scenes, making them susceptible to…
3D Semantic Occupancy PredictionAutonomous DrivingPredictionYouTube-Occ: Learning Indoor 3D Semantic Occupancy Prediction from YouTube Videos
3D semantic occupancy prediction in the past was considered to require precise geometric relationships in order to enable effective training. However, in complex indoor environments, the large-scale and widespread collec…
3D Semantic Occupancy PredictionRepresentation LearningSuperpixelsGraphGSOcc: Semantic-Geometric Graph Transformer with Dynamic-Static Decoupling for 3D Gaussian Splatting-based Occupancy Prediction
Addressing the task of 3D semantic occupancy prediction for autonomous driving, we tackle two key issues in existing 3D Gaussian Splatting (3DGS) methods: (1) unified feature aggregation neglecting semantic correlations …
3DGS3D Semantic Occupancy PredictionAutonomous DrivingGPU+1QuadricFormer: Scene as Superquadrics for 3D Semantic Occupancy Prediction
3D occupancy prediction is crucial for robust autonomous driving systems as it enables comprehensive perception of environmental structures and semantics. Most existing methods employ dense voxel-based scene representati…
3D Semantic Occupancy PredictionAutonomous DrivingPredictionVoxDet: Rethinking 3D Semantic Occupancy Prediction as Dense Object Detection
3D semantic occupancy prediction aims to reconstruct the 3D geometry and semantics of the surrounding environment. With dense voxel labels, prior works typically formulate it as a dense segmentation task, independently c…
3D geometry3D Semantic Occupancy PredictionDense Object Detectionobject-detection+1OccLE: Label-Efficient 3D Semantic Occupancy Prediction
3D semantic occupancy prediction offers an intuitive and efficient scene understanding and has attracted significant interest in autonomous driving perception. Existing approaches either rely on full supervision, which d…
3D Semantic Occupancy PredictionAutonomous DrivingMambaPrediction+1TACOcc:Target-Adaptive Cross-Modal Fusion with Volume Rendering for 3D Semantic Occupancy
The performance of multi-modal 3D occupancy prediction is limited by ineffective fusion, mainly due to geometry-semantics mismatch from fixed fusion strategies and surface detail loss caused by sparse, noisy annotations.…
3D Semantic Occupancy PredictionGaussianFormer3D: Multi-Modal Gaussian-based Semantic Occupancy Prediction with 3D Deformable Attention
3D semantic occupancy prediction is critical for achieving safe and reliable autonomous driving. Compared to camera-only perception systems, multi-modal pipelines, especially LiDAR-camera fusion methods, can produce more…
3D Semantic Occupancy PredictionAutonomous DrivingPredictionOccCylindrical: Multi-Modal Fusion with Cylindrical Representation for 3D Semantic Occupancy Prediction
The safe operation of autonomous vehicles (AVs) is highly dependent on their understanding of the surroundings. For this, the task of 3D semantic occupancy prediction divides the space around the sensors into voxels, and…
3D Semantic Occupancy PredictionAutonomous VehiclesLMPOcc: 3D Semantic Occupancy Prediction Utilizing Long-Term Memory Prior from Historical Traversals
Vision-based 3D semantic occupancy prediction is critical for autonomous driving, enabling unified modeling of static infrastructure and dynamic agents. In practice, autonomous vehicles may repeatedly traverse identical …
3D Semantic Occupancy PredictionAutonomous DrivingAutonomous VehiclesPredictionRethinking Temporal Fusion with a Unified Gradient Descent View for 3D Semantic Occupancy Prediction
We present GDFusion, a temporal fusion method for vision-based 3D semantic occupancy prediction (VisionOcc). GDFusion opens up the underexplored aspects of temporal fusion within the VisionOcc framework, focusing on both…
3D Semantic Occupancy PredictionAGO: Adaptive Grounding for Open World 3D Occupancy Prediction
Open-world 3D semantic occupancy prediction aims to generate a voxelized 3D representation from sensor inputs while recognizing both known and unknown objects. Transferring open-vocabulary knowledge from vision-language …
3D Semantic Occupancy PredictionPredictionInverse++: Vision-Centric 3D Semantic Occupancy Prediction Assisted with 3D Object Detection
3D semantic occupancy prediction aims to forecast detailed geometric and semantic information of the surrounding environment for autonomous vehicles (AVs) using onboard surround-view cameras. Existing methods primarily f…
3D Object Detection3D Semantic Occupancy PredictionAutonomous Vehiclesobject-detection+1MinkOcc: Towards real-time label-efficient semantic occupancy prediction
Developing 3D semantic occupancy prediction models often relies on dense 3D annotations for supervised learning, a process that is both labor and resource-intensive, underscoring the need for label-efficient or even labe…
3D Semantic Occupancy PredictionAutonomous DrivingPredictionSliceOcc: Indoor 3D Semantic Occupancy Prediction with Vertical Slice Representation
3D semantic occupancy prediction is a crucial task in visual perception, as it requires the simultaneous comprehension of both scene geometry and semantics. It plays a crucial role in understanding 3D scenes and has grea…
3D Semantic Occupancy PredictionAutonomous DrivingPredictionMR-Occ: Efficient Camera-LiDAR 3D Semantic Occupancy Prediction Using Hierarchical Multi-Resolution Voxel Representation
Accurate 3D perception is essential for understanding the environment in autonomous driving. Recent advancements in 3D semantic occupancy prediction have leveraged camera-LiDAR fusion to improve robustness and accuracy. …
3D Semantic Occupancy PredictionAutonomous DrivingDecoderGaussTR: Foundation Model-Aligned Gaussian Transformer for Self-Supervised 3D Spatial Understanding
3D Semantic Occupancy Prediction is fundamental for spatial understanding as it provides a comprehensive semantic cognition of surrounding environments. However, prevalent approaches primarily rely on extensive labeled d…
3D Semantic Occupancy PredictionAutonomous DrivingRepresentation LearningLOMA: Language-assisted Semantic Occupancy Network via Triplane Mamba
Vision-based 3D occupancy prediction has become a popular research task due to its versatility and affordability. Nowadays, conventional methods usually project the image-based vision features to 3D space and learn the g…
3D Semantic Occupancy PredictionMambaHierarchical Context Alignment with Disentangled Geometric and Temporal Modeling for Semantic Occupancy Prediction
Camera-based 3D Semantic Occupancy Prediction (SOP) is crucial for understanding complex 3D scenes from limited 2D image observations. Existing SOP methods typically aggregate contextual features to assist the occupancy …
3D Semantic Occupancy PredictionLIDAR Semantic SegmentationRepresentation LearningSemantic Segmentation