3D Semantic Occupancy Prediction
13 papers with code • 0 benchmarks • 1 datasets
Uses sparse LiDAR semantic labels for training and testing
Benchmarks
These leaderboards are used to track progress in 3D Semantic Occupancy Prediction
SimBEV
Most implemented papers
OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction
zhangyp15/occformer
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ICCV 2023
The vision-based perception for autonomous driving has undergone a transformation from the bird-eye-view (BEV) representations to the 3D semantic occupancy.
PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction
wzzheng/pointocc
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To address this, we propose a cylindrical tri-perspective view to represent point clouds effectively and comprehensively and a PointOcc model to process them efficiently.
InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction
danielming123/inversematrixvt3d
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In contrast, our approach leverages two projection matrices to store the static mapping relationships and matrix multiplications to efficiently generate global Bird's Eye View (BEV) features and local 3D feature volumes.
OccFusion: Multi-Sensor Fusion Framework for 3D Semantic Occupancy Prediction
A comprehensive understanding of 3D scenes is crucial in autonomous vehicles (AVs), and recent models for 3D semantic occupancy prediction have successfully addressed the challenge of describing real-world objects with varied shapes and classes.
Unleashing HyDRa: Hybrid Fusion, Depth Consistency and Radar for Unified 3D Perception
phi-wol/hydra
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HyDRa achieves a new state-of-the-art for camera-radar fusion of 64. 2 NDS (+1. 8) and 58. 4 AMOTA (+1. 5) on the public nuScenes dataset.
GaussianFormer: Scene as Gaussians for Vision-Based 3D Semantic Occupancy Prediction
To address this, we propose an object-centric representation to describe 3D scenes with sparse 3D semantic Gaussians where each Gaussian represents a flexible region of interest and its semantic features.
DAOcc: 3D Object Detection Assisted Multi-Sensor Fusion for 3D Occupancy Prediction
Multi-sensor fusion significantly enhances the accuracy and robustness of 3D semantic occupancy prediction, which is crucial for autonomous driving and robotics.
ALOcc: Adaptive Lifting-based 3D Semantic Occupancy and Cost Volume-based Flow Prediction
In this work, we strive to improve performance by introducing a series of targeted improvements for 3D semantic occupancy prediction and flow estimation.
Robust 3D Semantic Occupancy Prediction with Calibration-free Spatial Transformation
iceory/reo
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Recent methods are mainly built on the 2D-to-3D transformation that relies on sensor calibration to project the 2D image information into the 3D space.
GaussianFormer-2: Probabilistic Gaussian Superposition for Efficient 3D Occupancy Prediction
To address this, we propose a probabilistic Gaussian superposition model which interprets each Gaussian as a probability distribution of its neighborhood being occupied and conforms to probabilistic multiplication to derive the overall geometry.
