Occupancy Networks: Learning 3D Reconstruction in Function Space
The first work in the Occupancy Networks series, in this paper, the author proposes the Occupancy Network, a new learning-based three-dimensional reconstruction method. Currently cited【2275】
[1] 聊一聊Tesla 的Occupancy Network - 知乎 (zhihu.com)
[2]https://www.youtube.com/watch?v=ODSJsviD_SU&t=12s
[3]https://www.youtube.com/watch?v=KC8e0oTFUcw
1、 Collaborative Semantic Occupancy Prediction with Hybrid Feature Fusion in Connected Automated Vehicles[paper]
2、 Cam4DOcc: Benchmark for Camera-Only 4D Occupancy Forecasting in Autonomous Driving Applications[paper][code]
3、 Accurate Training Data for Occupancy Map Prediction in Automated Driving using Evidence Theory
4、 OccupancyM3D: Learning Occupancy for Monocular 3D Object Detection[paper][code]
5、 PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation[paper][code]
6、 COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy Prediction[paper]
7、 COTR: Compact Occupancy TRansformer for Vision-based 3D Occupancy Prediction[paper]
8 、 SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction[paper][code]
9 、 StreamingFlow: Streaming Occupancy Forecasting with Asynchronous Multi-modal Data Streams via Neural Ordinary Differential Equation
10 、 SGC-Occ: Semantic-Geometry Consistent 3D Occupancy Prediction for Autonomous Driving
11 、 SparseOcc: Rethinking Sparse Latent Representation for Vision-Based Semantic Occupancy Prediction
12 、 Unsupervised Occupancy Learning from Sparse Point Cloud[paper]
13 、 UnO: Unsupervised Occupancy Fields for Perception and Forecasting
14 、 Diffusion-FOF: Single-view Clothed Human Reconstruction via Diffusion-based Fourier Occupancy Field
1、[2024-01-17][NIPS2023] Pop-3d: Open-vocabulary 3d occupancy prediction from images[paper][code]
2、[2024-01-23] InverseMatrixVT3D: An Efficient Projection Matrix-Based Approach for 3D Occupancy Prediction[paper]
3、[2024-01-24] S2TPVFormer: Spatio-Temporal Tri-Perspective View for temporally coherent 3D Semantic Occupancy Prediction[paper]
4、[2024-02-20] OccFlowNet: Towards Self-supervised Occupancy Estimation via Differentiable Rendering and Occupancy Flow[paper]
5、[2024-03-03] OccFusion: A Straightforward and Effective Multi-Sensor Fusion Framework for 3D Occupancy Prediction[paper][code]
6、[2024-03-05][ICRA2024] FastOcc: Accelerating 3D Occupancy Prediction by Fusing the 2D Bird's-Eye View and Perspective View[paper]
1+、[2023-12-28] Fully Sparse 3D Occupancy Prediction[paper][code]
1、[2023-12-14][ICCV2023] OccNeRF：Rendering Humans from Object-Occluded Monocular Videos[paper][code]
2、[2023-12-09] OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using Octree Queries[paper]
3、[2023-11-29] SelfOcc: Self-Supervised Vision-Based 3D Occupancy Prediction[paper][code]
4、[2023-11-19] SOccDPT: Semi-Supervised 3D Semantic Occupancy from Dense Prediction Transformers trained under memory constraints[paper]
5、[2023-11-18] FlashOcc：Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin[paper]
6、[2023-11-16] A Simple Attempt for 3D Occupancy Estimation in Autonomous Driving[paper][code]
7、[2023-10-09] Occupancy-MAE：Occupancy-MAE: Self-Supervised Pre-Training Large-Scale LiDAR Point Clouds With Masked Occupancy Autoencoders[paper][code]
8、[2023-survey] Grid-Centric Traffic Scenario Perception for Autonomous Driving: A Comprehensive Review[paper]
9、[2023-10-09] Occ-BEV: Multi-Camera Unified Pre-training via 3D Scene Reconstruction[paper][code]
10、[2023-09-22] OccupancyDETR: Making Semantic Scene Completion as Straightforward as Object Detection[paper][code]
11、[2023-09-18] RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision[paper][code]
12、[2023-08-13] PointOcc: Cylindrical Tri-Perspective View for Point-based 3D Semantic Occupancy Prediction[paper][code]
13、[2023-08-27] SurroundOcc: Multi-Camera 3D Occupancy Prediction for Autonomous Driving[paper][code]
14、[2023-06-26][ICCV 2023] OccNet: Scene as Occupancy[paper][code]
15、[2023-06-16] PanoOcc: Unified Occupancy Representation for Camera-based 3D Panoptic Segmentation[paper][code]
16、[2023-06-15] UniOcc: Unifying Vision-Centric 3D Occupancy Prediction with Geometric and Semantic Rendering [paper]
17、[2023-05-25] OccupancyM3D: Learning Occupancy for Monocular 3D Object Detection[paper][code]
18、[2023-04-19][2023CVPR] Behind the Scenes: Density Fields for Single View Reconstruction【cite:13】[paper][code]
19、[2023-04-11] OccFormer: Dual-path Transformer for Vision-based 3D Semantic Occupancy Prediction[paper][code]
20、[2023-03-25] VoxFormer：Voxformer: Sparse voxel transformer for camera-based 3d semantic scene completion[paper][code]
21、[2023-03-02] TPVFormer：Tri-perspective view for vision-based 3d semantic occupancy prediction[paper][code]
22、[2023-02-27] OccDepth：Occdepth: A depth-aware method for 3d semantic scene completion[paper][code]
【2022】
1、[CVPR 2022] MonoScene: Monocular 3D Semantic Scene Completion[paper][code]
【2021】
1、Sparse Single Sweep LiDAR Point Cloud Segmentation via Learning Contextual Shape Priors from Scene Completion[paper]
In automatic driving according toenter the classification：（The following is the number of citations）As of 2024/01/04
single image：MonoScene【72】、OccupancyM3D【1】、VoxFormer【43】、A Simple Attempt for 3D Occupancy Estimation in Autonomous Driving【5】、OccupancyDETR【1】、SOccDPT【0】
depth image：2023-OccDepth【22】
multiview：TPVFormer【59】、SurroundOcc【31】、OccFormer【16】、OccBEV【2】、PanoOcc【6】、UniOcc【4】、SelfOcc【1】、OccNeRF【0】
lidar：PointOcc【2】
Occupancy-MAE: Self-supervised Pre-training Large-scale LiDAR Point Clouds with Masked Occupancy Autoencoders【8】
Occ-BEV【2】
FlashOcc：Fast and Memory-Efficient Occupancy Prediction via Channel-to-Height Plugin【0】
SOccDPT: Semi-Supervised 3D Semantic Occupancy from Dense Prediction Transformers trained under memory constraints【0】
OctreeOcc: Efficient and Multi-Granularity Occupancy Prediction Using Octree Queries
RenderOcc: Vision-Centric 3D Occupancy Prediction with 2D Rendering Supervision【4】
OccupancyDETR: Making Semantic Scene Completion as Straightforward as Object Detection【1】
OccNeRF: Self-Supervised Multi-Camera Occupancy Prediction with Neural Radiance Fields【0】
Behind the Scenes: Density Fields for Single View Reconstruction【13】
competition：The world's First 3D Occupancy Benchmark for Scene Perception in Autonomous Driving【https://github.com/CVPR2023-3D-Occupancy-Prediction/CVPR2023-3D-Occupancy-Prediction】
Occ3D: A Large-Scale 3D Occupancy Prediction Benchmark for Autonomous Driving 【32】
OpenOccupancy: A Large Scale Benchmark for Surrounding Semantic Occupancy Perception【15】
SurroundOcc【31】
SSCBench: A Large-Scale 3D Semantic Scene Completion Benchmark for Autonomous Driving【5】
Scene as Occupancy【15】
【2019-occupancy network】http://openaccess.thecvf.com/content_CVPR_2019/html/Mescheder_Occupancy_Networks_Learning_3D_Reconstruction_in_Function_Space_CVPR_2019_paper.html
【2022-MonoScene】http://openaccess.thecvf.com/content/CVPR2022/html/Cao_MonoScene_Monocular_3D_Semantic_Scene_Completion_CVPR_2022_paper.html
【2023-OccupancyM3D】https://arxiv.org/abs/2305.15694
【2023-OccDepth】https://arxiv.org/abs/2302.13540
【2023-SurroundOcc】http://openaccess.thecvf.com/content/ICCV2023/html/Wei_SurroundOcc_Multi-camera_3D_Occupancy_Prediction_for_Autonomous_Driving_ICCV_2023_paper.html
【2023-OccFormer】https://arxiv.org/abs/2304.05316
【2023-OccBEV】https://arxiv.org/abs/2305.18829
【2023-PanoOcc】https://arxiv.org/abs/2306.10013
【2022-LOPR】https://arxiv.org/abs/2210.01249
【A Simple Attempt for 3D Occupancy Estimation in Autonomous Driving】https://arxiv.org/abs/2303.10076
【2023-UniOcc】https://arxiv.org/abs/2306.09117
【2023-Occ3D】https://arxiv.org/abs/2304.14365
【2023-OpenOccupancy】https://arxiv.org/abs/2303.03991
【2023-SSCBench】https://arxiv.org/abs/2306.09001
【2023FlashOcc】https://arxiv.org/abs/2311.12058
【2023-OccupancyDETR】https://arxiv.org/abs/2309.08504
【2023-RenderOcc】https://arxiv.org/abs/2309.09502
【2023-PointOcc】https://arxiv.org/abs/2308.16896
【2023-OccNeRF】https://arxiv.org/abs/2312.09243
【2023-SelfOcc】https://arxiv.org/abs/2311.12754
【2023-Behind the Scenes】http://openaccess.thecvf.com/content/CVPR2023/html/Wimbauer_Behind_the_Scenes_Density_Fields_for_Single_View_Reconstruction_CVPR_2023_paper.html
【2023-SOccDPT】https://arxiv.org/abs/2311.11371
Jia Heng