Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation

• URL: http://arxiv.org/abs/2011.07945v1
• Date: Sun, 1 Nov 2020 17:41:32 GMT
• Title: Do not trust the neighbors! Adversarial Metric Learning for Self-Supervised Scene Flow Estimation
• Authors: Victor Zuanazzi
• Abstract summary: Scene flow is the task of estimating 3D motion vectors to individual points of a dynamic 3D scene. We propose a 3D scene flow benchmark and a novel self-supervised setup for training flow models. We find that our setup is able to keep motion coherence and preserve local geometries, which many self-supervised baselines fail to grasp.
• Score: 0.0
• License: http://creativecommons.org/publicdomain/zero/1.0/
• Abstract: Scene flow is the task of estimating 3D motion vectors to individual points of a dynamic 3D scene. Motion vectors have shown to be beneficial for downstream tasks such as action classification and collision avoidance. However, data collected via LiDAR sensors and stereo cameras are computation and labor intensive to precisely annotate for scene flow. We address this annotation bottleneck on two ends. We propose a 3D scene flow benchmark and a novel self-supervised setup for training flow models. The benchmark consists of datasets designed to study individual aspects of flow estimation in progressive order of complexity, from a single object in motion to real-world scenes. Furthermore, we introduce Adversarial Metric Learning for self-supervised flow estimation. The flow model is fed with sequences of point clouds to perform flow estimation. A second model learns a latent metric to distinguish between the points translated by the flow estimations and the target point cloud. This latent metric is learned via a Multi-Scale Triplet loss, which uses intermediary feature vectors for the loss calculation. We use our proposed benchmark to draw insights about the performance of the baselines and of different models when trained using our setup. We find that our setup is able to keep motion coherence and preserve local geometries, which many self-supervised baselines fail to grasp. Dealing with occlusions, on the other hand, is still an open challenge.

Related papers

• SeFlow: A Self-Supervised Scene Flow Method in Autonomous Driving [18.88208422580103]
  Scene flow estimation predicts the 3D motion at each point in successive LiDAR scans. Current state-of-the-art methods require annotated data to train scene flow networks. We propose SeFlow, a self-supervised method that integrates efficient dynamic classification into a learning-based scene flow pipeline.
  arXiv  Detail & Related papers  (2024-07-01T18:22:54Z)
• ICP-Flow: LiDAR Scene Flow Estimation with ICP [2.9290232815049926]
  Scene flow characterizes the 3D motion between two LiDAR scans captured by an autonomous vehicle at nearby timesteps. We propose ICP-Flow, a learning-free flow estimator, to associate objects over scans and then estimate the locally rigid transformations. We outperform state-of-the-art baselines, including supervised models, on the dataset and perform competitively on Argoverse-v2 and nuScenes.
  arXiv  Detail & Related papers  (2024-02-27T09:41:59Z)
• PointFlowHop: Green and Interpretable Scene Flow Estimation from Consecutive Point Clouds [49.7285297470392]
  An efficient 3D scene flow estimation method called PointFlowHop is proposed in this work. PointFlowHop takes two consecutive point clouds and determines the 3D flow vectors for every point in the first point cloud. It decomposes the scene flow estimation task into a set of subtasks, including ego-motion compensation, object association and object-wise motion estimation.
  arXiv  Detail & Related papers  (2023-02-27T23:06:01Z)
• SCOOP: Self-Supervised Correspondence and Optimization-Based Scene Flow [25.577386156273256]
  Scene flow estimation is a long-standing problem in computer vision, where the goal is to find the 3D motion of a scene from its consecutive observations. We introduce SCOOP, a new method for scene flow estimation that can be learned on a small amount of data without employing ground-truth flow supervision.
  arXiv  Detail & Related papers  (2022-11-25T10:52:02Z)
• Residual 3D Scene Flow Learning with Context-Aware Feature Extraction [11.394559627312743]
  We propose a novel context-aware set conv layer to exploit contextual structure information of Euclidean space. We also propose an explicit residual flow learning structure in the residual flow refinement layer to cope with long-distance movement. Our method achieves state-of-the-art performance, surpassing all other previous works to the best of our knowledge by at least 25%.
  arXiv  Detail & Related papers  (2021-09-10T06:15:18Z)
• Self-Point-Flow: Self-Supervised Scene Flow Estimation from Point Clouds with Optimal Transport and Random Walk [59.87525177207915]
  We develop a self-supervised method to establish correspondences between two point clouds to approximate scene flow. Our method achieves state-of-the-art performance among self-supervised learning methods.
  arXiv  Detail & Related papers  (2021-05-18T03:12:42Z)
• SCTN: Sparse Convolution-Transformer Network for Scene Flow Estimation [71.2856098776959]
  Estimating 3D motions for point clouds is challenging, since a point cloud is unordered and its density is significantly non-uniform. We propose a novel architecture named Sparse Convolution-Transformer Network (SCTN) that equips the sparse convolution with the transformer. We show that the learned relation-based contextual information is rich and helpful for matching corresponding points, benefiting scene flow estimation.
  arXiv  Detail & Related papers  (2021-05-10T15:16:14Z)
• Weakly Supervised Learning of Rigid 3D Scene Flow [81.37165332656612]
  We propose a data-driven scene flow estimation algorithm exploiting the observation that many 3D scenes can be explained by a collection of agents moving as rigid bodies. We showcase the effectiveness and generalization capacity of our method on four different autonomous driving datasets.
  arXiv  Detail & Related papers  (2021-02-17T18:58:02Z)
• FlowStep3D: Model Unrolling for Self-Supervised Scene Flow Estimation [87.74617110803189]
  Estimating the 3D motion of points in a scene, known as scene flow, is a core problem in computer vision. We present a recurrent architecture that learns a single step of an unrolled iterative alignment procedure for refining scene flow predictions.
  arXiv  Detail & Related papers  (2020-11-19T23:23:48Z)
• Adversarial Self-Supervised Scene Flow Estimation [15.278302535191866]
  This work proposes a metric learning approach for self-supervised scene flow estimation. We outline a benchmark for self-supervised scene flow estimation: the Scene Flow Sandbox.
  arXiv  Detail & Related papers  (2020-11-01T16:37:37Z)
• Self-Supervised Learning of Non-Rigid Residual Flow and Ego-Motion [63.18340058854517]
  We present an alternative method for end-to-end scene flow learning by joint estimation of non-rigid residual flow and ego-motion flow for dynamic 3D scenes. We extend the supervised framework with self-supervisory signals based on the temporal consistency property of a point cloud sequence.
  arXiv  Detail & Related papers  (2020-09-22T11:39:19Z)

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.