Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across
Objects and Views
- URL: http://arxiv.org/abs/2109.07945v1
- Date: Thu, 16 Sep 2021 13:01:13 GMT
- Title: Lifting 2D Object Locations to 3D by Discounting LiDAR Outliers across
Objects and Views
- Authors: Robert McCraith, Eldar Insafudinov, Lukas Neumann, Andrea Vedaldi
- Abstract summary: We present a system for automatically converting 2D mask object predictions and raw LiDAR point clouds into full 3D bounding boxes of objects.
Our method significantly outperforms previous work despite the fact that those methods use significantly more complex pipelines, 3D models and additional human-annotated external sources of prior information.
- Score: 70.1586005070678
- License: http://creativecommons.org/licenses/by/4.0/
- Abstract: We present a system for automatic converting of 2D mask object predictions
and raw LiDAR point clouds into full 3D bounding boxes of objects. Because the
LiDAR point clouds are partial, directly fitting bounding boxes to the point
clouds is meaningless. Instead, we suggest that obtaining good results requires
sharing information between \emph{all} objects in the dataset jointly, over
multiple frames. We then make three improvements to the baseline. First, we
address ambiguities in predicting the object rotations via direct optimization
in this space while still backpropagating rotation prediction through the
model. Second, we explicitly model outliers and task the network with learning
their typical patterns, thus better discounting them. Third, we enforce
temporal consistency when video data is available. With these contributions,
our method significantly outperforms previous work despite the fact that those
methods use significantly more complex pipelines, 3D models and additional
human-annotated external sources of prior information.
Related papers
- Inverse Neural Rendering for Explainable Multi-Object Tracking [35.072142773300655]
We recast 3D multi-object tracking from RGB cameras as an emphInverse Rendering (IR) problem.
We optimize an image loss over generative latent spaces that inherently disentangle shape and appearance properties.
We validate the generalization and scaling capabilities of our method by learning the generative prior exclusively from synthetic data.
arXiv Detail & Related papers (2024-04-18T17:37:53Z) - Leveraging Large-Scale Pretrained Vision Foundation Models for
Label-Efficient 3D Point Cloud Segmentation [67.07112533415116]
We present a novel framework that adapts various foundational models for the 3D point cloud segmentation task.
Our approach involves making initial predictions of 2D semantic masks using different large vision models.
To generate robust 3D semantic pseudo labels, we introduce a semantic label fusion strategy that effectively combines all the results via voting.
arXiv Detail & Related papers (2023-11-03T15:41:15Z) - LEF: Late-to-Early Temporal Fusion for LiDAR 3D Object Detection [40.267769862404684]
We propose a late-to-early recurrent feature fusion scheme for 3D object detection using temporal LiDAR point clouds.
Our main motivation is fusing object-aware latent embeddings into the early stages of a 3D object detector.
arXiv Detail & Related papers (2023-09-28T21:58:25Z) - CAGroup3D: Class-Aware Grouping for 3D Object Detection on Point Clouds [55.44204039410225]
We present a novel two-stage fully sparse convolutional 3D object detection framework, named CAGroup3D.
Our proposed method first generates some high-quality 3D proposals by leveraging the class-aware local group strategy on the object surface voxels.
To recover the features of missed voxels due to incorrect voxel-wise segmentation, we build a fully sparse convolutional RoI pooling module.
arXiv Detail & Related papers (2022-10-09T13:38:48Z) - DETR3D: 3D Object Detection from Multi-view Images via 3D-to-2D Queries [43.02373021724797]
We introduce a framework for multi-camera 3D object detection.
Our method manipulates predictions directly in 3D space.
We achieve state-of-the-art performance on the nuScenes autonomous driving benchmark.
arXiv Detail & Related papers (2021-10-13T17:59:35Z) - Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR-based
Perception [122.53774221136193]
State-of-the-art methods for driving-scene LiDAR-based perception often project the point clouds to 2D space and then process them via 2D convolution.
A natural remedy is to utilize the 3D voxelization and 3D convolution network.
We propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pattern.
arXiv Detail & Related papers (2021-09-12T06:25:11Z) - Cylindrical and Asymmetrical 3D Convolution Networks for LiDAR
Segmentation [81.02742110604161]
State-of-the-art methods for large-scale driving-scene LiDAR segmentation often project the point clouds to 2D space and then process them via 2D convolution.
We propose a new framework for the outdoor LiDAR segmentation, where cylindrical partition and asymmetrical 3D convolution networks are designed to explore the 3D geometric pat-tern.
Our method achieves the 1st place in the leaderboard of Semantic KITTI and outperforms existing methods on nuScenes with a noticeable margin, about 4%.
arXiv Detail & Related papers (2020-11-19T18:53:11Z) - DOPS: Learning to Detect 3D Objects and Predict their 3D Shapes [54.239416488865565]
We propose a fast single-stage 3D object detection method for LIDAR data.
The core novelty of our method is a fast, single-pass architecture that both detects objects in 3D and estimates their shapes.
We find that our proposed method achieves state-of-the-art results by 5% on object detection in ScanNet scenes, and it gets top results by 3.4% in the Open dataset.
arXiv Detail & Related papers (2020-04-02T17:48:50Z)
This list is automatically generated from the titles and abstracts of the papers in this site.
This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.