Floxels: Fast Unsupervised Voxel Based Scene Flow Estimation

• URL: http://arxiv.org/abs/2503.04718v2
• Date: Thu, 03 Apr 2025 18:17:18 GMT
• Title: Floxels: Fast Unsupervised Voxel Based Scene Flow Estimation
• Authors: David T. Hoffmann, Syed Haseeb Raza, Hanqiu Jiang, Denis Tananaev, Steffen Klingenhoefer, Martin Meinke,
• Abstract summary: Two types of approaches to the problem have evolved: 1) Supervised and 2) optimization-based methods.<n>Floxels is surpassed only by EulerFlow among unsupervised methods while achieving comparable performance at a fraction of the computational cost.<n>Floxels achieves a massive speedup of more than 60 - 140x over EulerFlow, reducing the runtime from a day to 10 minutes per sequence.
• Score: 1.429392440481971
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Scene flow estimation is a foundational task for many robotic applications, including robust dynamic object detection, automatic labeling, and sensor synchronization. Two types of approaches to the problem have evolved: 1) Supervised and 2) optimization-based methods. Supervised methods are fast during inference and achieve high-quality results, however, they are limited by the need for large amounts of labeled training data and are susceptible to domain gaps. In contrast, unsupervised test-time optimization methods do not face the problem of domain gaps but usually suffer from substantial runtime, exhibit artifacts, or fail to converge to the right solution. In this work, we mitigate several limitations of existing optimization-based methods. To this end, we 1) introduce a simple voxel grid-based model that improves over the standard MLP-based formulation in multiple dimensions and 2) introduce a new multiframe loss formulation. 3) We combine both contributions in our new method, termed Floxels. On the Argoverse 2 benchmark, Floxels is surpassed only by EulerFlow among unsupervised methods while achieving comparable performance at a fraction of the computational cost. Floxels achieves a massive speedup of more than ~60 - 140x over EulerFlow, reducing the runtime from a day to 10 minutes per sequence. Over the faster but low-quality baseline, NSFP, Floxels achieves a speedup of ~14x.

Related papers

• ScoreFlow: Mastering LLM Agent Workflows via Score-based Preference Optimization [51.280919773837645]
  We develop ScoreFlow, a high-performance framework for agent workflow optimization.<n>ScoreFlow incorporates Score-DPO, a novel variant of the direct preference optimization method that accounts for quantitative feedback.<n>It achieves an 8.2% improvement over existing baselines across question answering, coding, and mathematical reasoning.
  arXiv  Detail & Related papers  (2025-02-06T18:47:49Z)
• DiffIM: Differentiable Influence Minimization with Surrogate Modeling and Continuous Relaxation [23.06479920145709]
  Influence minimization (IMIN) is the problem of manipulating the structures of an input graph to reduce the propagation among nodes.<n>We propose DiffIM, a novel method for IMIN with two differentiable schemes for acceleration.<n>We show that each proposed scheme significantly improves speed with little (or even no) IMIN performance degradation.
  arXiv  Detail & Related papers  (2025-02-03T03:54:23Z)
• Dual-frame Fluid Motion Estimation with Test-time Optimization and Zero-divergence Loss [9.287932323337163]
  3D particle tracking velocimetry (PTV) is a key technique for analyzing turbulent flow. Deep learning-based methods have achieved impressive accuracy in dual-frame fluid motion estimation. We introduce a new method that is completely self-supervised and notably outperforms its fully-supervised counterparts.
  arXiv  Detail & Related papers  (2024-10-15T18:00:00Z)
• Consistency Flow Matching: Defining Straight Flows with Velocity Consistency [97.28511135503176]
  We introduce Consistency Flow Matching (Consistency-FM), a novel FM method that explicitly enforces self-consistency in the velocity field. Preliminary experiments demonstrate that our Consistency-FM significantly improves training efficiency by converging 4.4x faster than consistency models.
  arXiv  Detail & Related papers  (2024-07-02T16:15:37Z)
• FuzzyFlow: Leveraging Dataflow To Find and Squash Program Optimization Bugs [92.47146416628965]
  FuzzyFlow is a fault localization and test case extraction framework designed to test program optimizations. We leverage dataflow program representations to capture a fully reproducible system state and area-of-effect for optimizations. To reduce testing time, we design an algorithm for minimizing test inputs, trading off memory for recomputation.
  arXiv  Detail & Related papers  (2023-06-28T13:00:17Z)
• ZeroFlow: Scalable Scene Flow via Distillation [66.70820145266029]
  Scene flow estimation is the task of describing the 3D motion field between temporally successive point clouds. State-of-the-art methods use strong priors and test-time optimization techniques, but require on the order of tens of seconds to process full-size point clouds. We propose Scene Flow via Distillation, a simple, scalable distillation framework that uses a label-free optimization method to produce pseudo-labels to supervise a feedforward model.
  arXiv  Detail & Related papers  (2023-05-17T17:56:59Z)
• Explicit Second-Order Min-Max Optimization Methods with Optimal Convergence Guarantee [86.05440220344755]
  We propose and analyze inexact regularized Newton-type methods for finding a global saddle point of emphcon unconstrained min-max optimization problems. We show that the proposed methods generate iterates that remain within a bounded set and that the iterations converge to an $epsilon$-saddle point within $O(epsilon-2/3)$ in terms of a restricted function.
  arXiv  Detail & Related papers  (2022-10-23T21:24:37Z)
• Multipoint-BAX: A New Approach for Efficiently Tuning Particle Accelerator Emittance via Virtual Objectives [47.52324722637079]
  We propose a new information-theoretic algorithm, Multipoint-BAX, for black-box optimization on multipoint queries. We use Multipoint-BAX to minimize emittance at the Linac Coherent Light Source (LCLS) and the Facility for Advanced Accelerator Experimental Tests II (FACET-II)
  arXiv  Detail & Related papers  (2022-09-10T04:01:23Z)
• Fast and Robust Non-Rigid Registration Using Accelerated Majorization-Minimization [35.66014845211251]
  Non-rigid registration, which deforms a source shape in a non-rigid way to align with a target shape, is a classical problem in computer vision. Existing methods typically adopt the $ell_p$ type robust norm to measure the alignment error and regularize the smoothness of deformation. We propose a formulation for robust non-rigid registration based on a globally smooth robust norm for alignment and regularization.
  arXiv  Detail & Related papers  (2022-06-07T16:00:33Z)
• Self Normalizing Flows [65.73510214694987]
  We propose a flexible framework for training normalizing flows by replacing expensive terms in the gradient by learned approximate inverses at each layer. This reduces the computational complexity of each layer's exact update from $mathcalO(D3)$ to $mathcalO(D2)$. We show experimentally that such models are remarkably stable and optimize to similar data likelihood values as their exact gradient counterparts.
  arXiv  Detail & Related papers  (2020-11-14T09:51:51Z)

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.