SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments

• URL: http://arxiv.org/abs/2503.14837v1
• Date: Wed, 19 Mar 2025 02:43:19 GMT
• Title: SemanticFlow: A Self-Supervised Framework for Joint Scene Flow Prediction and Instance Segmentation in Dynamic Environments
• Authors: Yinqi Chen, Meiying Zhang, Qi Hao, Guang Zhou,
• Abstract summary: This paper proposes a multi-task framework to simultaneously predict scene flow and instance segmentation of full-temporal point clouds.<n>The novelty of this work is threefold: 1) developing a coarse-to-fine prediction based multitask scheme, where an initial coarse segmentation of static backgrounds and dynamic objects is used to provide contextual information for refining motion and semantic information through a shared feature processing module; 2) developing a set of loss functions to enhance the performance of scene flow estimation and instance segmentation, while can help ensure spatial and temporal consistency of both static and dynamic objects within traffic scenes; 3) developing a self-supervised learning scheme, which utilizes coarse
• Score: 10.303368447554591
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Accurate perception of dynamic traffic scenes is crucial for high-level autonomous driving systems, requiring robust object motion estimation and instance segmentation. However, traditional methods often treat them as separate tasks, leading to suboptimal performance, spatio-temporal inconsistencies, and inefficiency in complex scenarios due to the absence of information sharing. This paper proposes a multi-task SemanticFlow framework to simultaneously predict scene flow and instance segmentation of full-resolution point clouds. The novelty of this work is threefold: 1) developing a coarse-to-fine prediction based multi-task scheme, where an initial coarse segmentation of static backgrounds and dynamic objects is used to provide contextual information for refining motion and semantic information through a shared feature processing module; 2) developing a set of loss functions to enhance the performance of scene flow estimation and instance segmentation, while can help ensure spatial and temporal consistency of both static and dynamic objects within traffic scenes; 3) developing a self-supervised learning scheme, which utilizes coarse segmentation to detect rigid objects and compute their transformation matrices between sequential frames, enabling the generation of self-supervised labels. The proposed framework is validated on the Argoverse and Waymo datasets, demonstrating superior performance in instance segmentation accuracy, scene flow estimation, and computational efficiency, establishing a new benchmark for self-supervised methods in dynamic scene understanding.

Related papers

• Mitigating Trade-off: Stream and Query-guided Aggregation for Efficient and Effective 3D Occupancy Prediction [12.064509280163502]
  3D occupancy prediction has emerged as a key perception task for autonomous driving. Recent studies focus on integrating information obtained from past observations to improve prediction accuracy. We propose StreamOcc, a framework that aggregates past-temporal information in a stream-based manner. Experiments on the Occ3D-nus dataset show that StreamOcc achieves state-of-the-art performance in real-time settings, while reducing memory usage by more than 50% compared to previous methods.
  arXiv  Detail & Related papers  (2025-03-28T02:05:53Z)
• EgoSplat: Open-Vocabulary Egocentric Scene Understanding with Language Embedded 3D Gaussian Splatting [108.15136508964011]
  EgoSplat is a language-embedded 3D Gaussian Splatting framework for open-vocabulary egocentric scene understanding.<n>EgoSplat achieves state-of-the-art performance in both localization and segmentation tasks on two datasets.
  arXiv  Detail & Related papers  (2025-03-14T12:21:26Z)
• Learning Motion and Temporal Cues for Unsupervised Video Object Segmentation [49.113131249753714]
  We propose an efficient algorithm, termed MTNet, which concurrently exploits motion and temporal cues. MTNet is devised by effectively merging appearance and motion features during the feature extraction process within encoders. We employ a cascade of decoders all feature levels across all feature levels to optimally exploit the derived features.
  arXiv  Detail & Related papers  (2025-01-14T03:15:46Z)
• Static-Dynamic Class-level Perception Consistency in Video Semantic Segmentation [9.964615076037397]
  Video semantic segmentation (VSS) has been widely employed in lots of fields, such as simultaneous localization and mapping.<n>Previous efforts have primarily focused on pixel-level static-dynamic contexts matching.<n>This paper rethinks static-dynamic contexts at the class level and proposes a novel static-dynamic class-level perceptual consistency framework.
  arXiv  Detail & Related papers  (2024-12-11T02:29:51Z)
• 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)
• Appearance-Based Refinement for Object-Centric Motion Segmentation [85.2426540999329]
  We introduce an appearance-based refinement method that leverages temporal consistency in video streams to correct inaccurate flow-based proposals. Our approach involves a sequence-level selection mechanism that identifies accurate flow-predicted masks as exemplars. Its performance is evaluated on multiple video segmentation benchmarks, including DAVIS, YouTube, SegTrackv2, and FBMS-59.
  arXiv  Detail & Related papers  (2023-12-18T18:59:51Z)
• Multi-body SE(3) Equivariance for Unsupervised Rigid Segmentation and Motion Estimation [49.56131393810713]
  We present an SE(3) equivariant architecture and a training strategy to tackle this task in an unsupervised manner. Our method excels in both model performance and computational efficiency, with only 0.25M parameters and 0.92G FLOPs.
  arXiv  Detail & Related papers  (2023-06-08T22:55:32Z)
• A Dynamic Feature Interaction Framework for Multi-task Visual Perception [100.98434079696268]
  We devise an efficient unified framework to solve multiple common perception tasks. These tasks include instance segmentation, semantic segmentation, monocular 3D detection, and depth estimation. Our proposed framework, termed D2BNet, demonstrates a unique approach to parameter-efficient predictions for multi-task perception.
  arXiv  Detail & Related papers  (2023-06-08T09:24:46Z)
• Motion-state Alignment for Video Semantic Segmentation [4.375012768093524]
  We propose a novel motion-state alignment framework for video semantic segmentation. The proposed method picks up dynamic and static semantics in a targeted way. Experiments on Cityscapes and CamVid datasets show that the proposed approach outperforms state-of-the-art methods.
  arXiv  Detail & Related papers  (2023-04-18T08:34:46Z)
• Dyna-DepthFormer: Multi-frame Transformer for Self-Supervised Depth Estimation in Dynamic Scenes [19.810725397641406]
  We propose a novel Dyna-Depthformer framework, which predicts scene depth and 3D motion field jointly. Our contributions are two-fold. First, we leverage multi-view correlation through a series of self- and cross-attention layers in order to obtain enhanced depth feature representation. Second, we propose a warping-based Motion Network to estimate the motion field of dynamic objects without using semantic prior.
  arXiv  Detail & Related papers  (2023-01-14T09:43:23Z)
• Learning to Segment Rigid Motions from Two Frames [72.14906744113125]
  We propose a modular network, motivated by a geometric analysis of what independent object motions can be recovered from an egomotion field. It takes two consecutive frames as input and predicts segmentation masks for the background and multiple rigidly moving objects, which are then parameterized by 3D rigid transformations. Our method achieves state-of-the-art performance for rigid motion segmentation on KITTI and Sintel.
  arXiv  Detail & Related papers  (2021-01-11T04:20:30Z)

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.