3D-VField: Learning to Adversarially Deform Point Clouds for Robust 3D Object Detection

• URL: http://arxiv.org/abs/2112.04764v1
• Date: Thu, 9 Dec 2021 08:50:54 GMT
• Title: 3D-VField: Learning to Adversarially Deform Point Clouds for Robust 3D Object Detection
• Authors: Alexander Lehner, Stefano Gasperini, Alvaro Marcos-Ramiro, Michael Schmidt, Mohammad-Ali Nikouei Mahani, Nassir Navab, Benjamin Busam, Federico Tombari
• Abstract summary: In safety-critical settings, robustness on out-of-distribution and long-tail samples is fundamental to circumvent dangerous issues. We substantially improve the generalization of 3D object detectors to out-of-domain data by taking into account deformed point clouds during training. We propose and share open source CrashD: a synthetic dataset of realistic damaged and rare cars.
• Score: 111.32054128362427
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: As 3D object detection on point clouds relies on the geometrical relationships between the points, non-standard object shapes can hinder a method's detection capability. However, in safety-critical settings, robustness on out-of-distribution and long-tail samples is fundamental to circumvent dangerous issues, such as the misdetection of damaged or rare cars. In this work, we substantially improve the generalization of 3D object detectors to out-of-domain data by taking into account deformed point clouds during training. We achieve this with 3D-VField: a novel method that plausibly deforms objects via vectors learned in an adversarial fashion. Our approach constrains 3D points to slide along their sensor view rays while neither adding nor removing any of them. The obtained vectors are transferrable, sample-independent and preserve shape smoothness and occlusions. By augmenting normal samples with the deformations produced by these vector fields during training, we significantly improve robustness against differently shaped objects, such as damaged/deformed cars, even while training only on KITTI. Towards this end, we propose and share open source CrashD: a synthetic dataset of realistic damaged and rare cars, with a variety of crash scenarios. Extensive experiments on KITTI, Waymo, our CrashD and SUN RGB-D show the high generalizability of our techniques to out-of-domain data, different models and sensors, namely LiDAR and ToF cameras, for both indoor and outdoor scenes. Our CrashD dataset is available at https://crashd-cars.github.io.

Related papers

• 3D Adversarial Augmentations for Robust Out-of-Domain Predictions [115.74319739738571]
  We focus on improving the generalization to out-of-domain data. We learn a set of vectors that deform the objects in an adversarial fashion. We perform adversarial augmentation by applying the learned sample-independent vectors to the available objects when training a model.
  arXiv  Detail & Related papers  (2023-08-29T17:58:55Z)
• FocalFormer3D : Focusing on Hard Instance for 3D Object Detection [97.56185033488168]
  False negatives (FN) in 3D object detection can lead to potentially dangerous situations in autonomous driving. In this work, we propose Hard Instance Probing (HIP), a general pipeline that identifies textitFN in a multi-stage manner. We instantiate this method as FocalFormer3D, a simple yet effective detector that excels at excavating difficult objects.
  arXiv  Detail & Related papers  (2023-08-08T20:06:12Z)
• On the Robustness of 3D Object Detectors [9.467525852900007]
  3D scenes exhibit a lot of variations and are prone to sensor inaccuracies as well as information loss during pre-processing. This work aims to analyze and benchmark popular point-based 3D object detectors against several data corruptions.
  arXiv  Detail & Related papers  (2022-07-20T21:47:15Z)
• Embracing Single Stride 3D Object Detector with Sparse Transformer [63.179720817019096]
  In LiDAR-based 3D object detection for autonomous driving, the ratio of the object size to input scene size is significantly smaller compared to 2D detection cases. Many 3D detectors directly follow the common practice of 2D detectors, which downsample the feature maps even after quantizing the point clouds. We propose Single-stride Sparse Transformer (SST) to maintain the original resolution from the beginning to the end of the network.
  arXiv  Detail & Related papers  (2021-12-13T02:12:02Z)
• ST3D: Self-training for Unsupervised Domain Adaptation on 3D ObjectDetection [78.71826145162092]
  We present a new domain adaptive self-training pipeline, named ST3D, for unsupervised domain adaptation on 3D object detection from point clouds. Our ST3D achieves state-of-the-art performance on all evaluated datasets and even surpasses fully supervised results on KITTI 3D object detection benchmark.
  arXiv  Detail & Related papers  (2021-03-09T10:51:24Z)
• 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.