Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions

• URL: http://arxiv.org/abs/2203.12193v1
• Date: Wed, 23 Mar 2022 04:53:26 GMT
• Title: Self-Supervised Robust Scene Flow Estimation via the Alignment of Probability Density Functions
• Abstract summary: We present a new self-supervised scene flow estimation approach for a pair of consecutive point clouds. Our method makes noticeable gains over the Chamfer Distance and the Earth Mover's Distance in real-world environments.
• Score: 11.69144204466843
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: In this paper, we present a new self-supervised scene flow estimation approach for a pair of consecutive point clouds. The key idea of our approach is to represent discrete point clouds as continuous probability density functions using Gaussian mixture models. Scene flow estimation is therefore converted into the problem of recovering motion from the alignment of probability density functions, which we achieve using a closed-form expression of the classic Cauchy-Schwarz divergence. Unlike existing nearest-neighbor-based approaches that use hard pairwise correspondences, our proposed approach establishes soft and implicit point correspondences between point clouds and generates more robust and accurate scene flow in the presence of missing correspondences and outliers. Comprehensive experiments show that our method makes noticeable gains over the Chamfer Distance and the Earth Mover's Distance in real-world environments and achieves state-of-the-art performance among self-supervised learning methods on FlyingThings3D and KITTI, even outperforming some supervised methods with ground truth annotations.

Related papers

• Cross-Coordinate Correspondence Pruning for Image-to-Point Cloud Registration [70.44225088078274]
  We propose a novel Cross-Coordinate Correspondences Pruning (CCP) strategy to acquire sufficient inliers while ensuring a low outlier ratio.<n>Our method achieves a significant performance improvement, surpassing existing state-of-the-art methods by at least 8.6% in Registration Recall across various benchmarks.
  arXiv  Detail & Related papers  (2026-07-19T11:45:14Z)
• A Unified Measure-Theoretic View of Diffusion, Score-Based, and Flow Matching Generative Models [0.3222802562733787]
  We survey continuous-time generative modeling methods based on transporting a simple reference distribution to a data distribution via deterministic dynamics.<n>We present a unified framework in which diffusion models, score-based generative models, and flow matching are instances of learning a time-dependent vector field.
  arXiv  Detail & Related papers  (2026-05-07T18:32:15Z)
• GeodesicNVS: Probability Density Geodesic Flow Matching for Novel View Synthesis [54.39598154430305]
  We propose a Data-to-Data Flow Matching framework that learns deterministic transformations directly between paired views.<n>PDG-FM constrains flow trajectories using geodesic interpolants derived from probability density metrics of pretrained diffusion models.<n>These results highlight the advantages of incorporating data-dependent geometric regularization into deterministic flow matching for consistent novel view generation.
  arXiv  Detail & Related papers  (2026-03-01T09:30:11Z)
• Efficient Point Clouds Upsampling via Flow Matching [16.948354780275388]
  Existing diffusion models struggle with inefficiencies as they map Gaussian noise to real point clouds. We propose PUFM, a flow matching approach to directly map sparse point clouds to their high-fidelity dense counterparts. Our method delivers superior upsampling quality but with fewer sampling steps.
  arXiv  Detail & Related papers  (2025-01-25T17:50:53Z)
• Deterministic Fokker-Planck Transport -- With Applications to Sampling, Variational Inference, Kernel Mean Embeddings & Sequential Monte Carlo [0.0]
  We reformulate the Fokker-Planck equation as a continuity equation, which naturally suggests using the associated velocity field in particle flow methods. While the resulting probability flow ODE offers appealing properties, it relies on evaluating the current probability density, which is intractable in most practical applications. We uncover opportunities to turn these limitations into advantages in contexts such as variational inference, kernel mean embeddings, and sequential Monte Carlo.
  arXiv  Detail & Related papers  (2024-10-11T13:06:36Z)
• Unlearning-based Neural Interpretations [51.99182464831169]
  We show that current baselines defined using static functions are biased, fragile and manipulable. We propose UNI to compute an (un)learnable, debiased and adaptive baseline by perturbing the input towards an unlearning direction of steepest ascent.
  arXiv  Detail & Related papers  (2024-10-10T16:02:39Z)
• Spatially-Aware Diffusion Models with Cross-Attention for Global Field Reconstruction with Sparse Observations [1.371691382573869]
  We develop and enhance score-based diffusion models in field reconstruction tasks. We introduce a condition encoding approach to construct a tractable mapping mapping between observed and unobserved regions. We demonstrate the ability of the model to capture possible reconstructions and improve the accuracy of fused results.
  arXiv  Detail & Related papers  (2024-08-30T19:46:23Z)
• Efficient Trajectory Inference in Wasserstein Space Using Consecutive Averaging [3.8623569699070353]
  Trajectory inference deals with the challenge of reconstructing continuous processes from such observations. We propose methods for B-spline approximation of point clouds through consecutive averaging that is instrinsic to the Wasserstein space. We rigorously evaluate our method by providing convergence guarantees and testing it on simulated cell data.
  arXiv  Detail & Related papers  (2024-05-30T04:19:20Z)
• Self-Supervised Class-Agnostic Motion Prediction with Spatial and Temporal Consistency Regularizations [53.797896854533384]
  Class-agnostic motion prediction methods directly predict the motion of the entire point cloud. While most existing methods rely on fully-supervised learning, the manual labeling of point cloud data is laborious and time-consuming. We introduce three simple spatial and temporal regularization losses, which facilitate the self-supervised training process effectively.
  arXiv  Detail & Related papers  (2024-03-20T02:58:45Z)
• Observation-Guided Diffusion Probabilistic Models [41.749374023639156]
  We propose a novel diffusion-based image generation method called the observation-guided diffusion probabilistic model (OGDM) Our approach reestablishes the training objective by integrating the guidance of the observation process with the Markov chain. We demonstrate the effectiveness of our training algorithm using diverse inference techniques on strong diffusion model baselines.
  arXiv  Detail & Related papers  (2023-10-06T06:29:06Z)
• PDC-Net+: Enhanced Probabilistic Dense Correspondence Network [161.76275845530964]
  Enhanced Probabilistic Dense Correspondence Network, PDC-Net+, capable of estimating accurate dense correspondences. We develop an architecture and an enhanced training strategy tailored for robust and generalizable uncertainty prediction. Our approach obtains state-of-the-art results on multiple challenging geometric matching and optical flow datasets.
  arXiv  Detail & Related papers  (2021-09-28T17:56:41Z)
• PU-Flow: a Point Cloud Upsampling Networkwith Normalizing Flows [58.96306192736593]
  We present PU-Flow, which incorporates normalizing flows and feature techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we formulate the upsampling process as point in a latent space, where the weights are adaptively learned from local geometric context. We show that our method outperforms state-of-the-art deep learning-based approaches in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency.
  arXiv  Detail & Related papers  (2021-07-13T07:45:48Z)
• 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)

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.