Raymoval: Raycasting-based Dynamic Object Removal for Static 3D Mapping

• URL: http://arxiv.org/abs/2605.08937v1
• Date: Sat, 09 May 2026 13:15:59 GMT
• Title: Raymoval: Raycasting-based Dynamic Object Removal for Static 3D Mapping
• Abstract summary: We propose a raycasting-based module for dynamic object removal in static 3D mapping.<n>Each scan is projected onto an azimuth-elevation grid, and for every viewing direction we compare the bin-wise minimum range with the map's first-hit distance computed by raycasting.<n>Finally, a spatial consistency validation step refines labels, producing static maps with lower residual dynamics and reduced over-removal.
• Score: 8.410344664123071
• License: http://creativecommons.org/licenses/by-nc-nd/4.0/
• Abstract: Static mapping is fundamental to robot navigation, providing a persistent geometric prior and a consistent reference for long-term autonomy. However, dynamic objects leave residual traces and cause surface loss, which reduces map consistency. We propose a raycasting-based module for dynamic object removal in static 3D mapping. Each scan is projected onto an azimuth-elevation grid, and for every viewing direction we compare the bin-wise minimum range with the map's first-hit distance computed by raycasting. Furthermore, we apply a raycast consistency test that separates dynamic from static points. Finally, a spatial consistency validation step refines labels, producing static maps with lower residual dynamics and reduced over-removal. We evaluate our approach quantitatively and qualitatively on SemanticKITTI and a challenging custom dataset, and show consistent static mapping results.

Related papers

• RayMap3R: Inference-Time RayMap for Dynamic 3D Reconstruction [22.98233376526841]
  Streaming feed-forward 3D reconstruction enables real-time joint estimation of scene geometry and camera poses from RGB images.<n>We propose RayMap3R, a training-free streaming framework for dynamic scene reconstruction.
  arXiv  Detail & Related papers  (2026-03-21T01:04:31Z)
• D$^2$USt3R: Enhancing 3D Reconstruction for Dynamic Scenes [54.886845755635754]
  This work addresses the task of 3D reconstruction in dynamic scenes, where object motions frequently degrade the quality of previous 3D pointmap regression methods.<n>By explicitly incorporating both spatial and temporal aspects, our approach successfully encapsulates 3D dense correspondence to the proposed pointmaps.
  arXiv  Detail & Related papers  (2025-04-08T17:59:50Z)
• POMATO: Marrying Pointmap Matching with Temporal Motion for Dynamic 3D Reconstruction [53.19968902152528]
  We present POMATO, a unified framework for dynamic 3D reconstruction by marrying pointmap matching with temporal motion.<n>Specifically, our method learns an explicit matching relationship by mapping RGB pixels from both dynamic and static regions across different views to 3D pointmaps.<n>We show the effectiveness of the proposed pointmap matching and temporal fusion paradigm by demonstrating the remarkable performance across multiple downstream tasks.
  arXiv  Detail & Related papers  (2025-04-08T05:33:13Z)
• Dynamic Point Maps: A Versatile Representation for Dynamic 3D Reconstruction [56.32589034046427]
  We introduce Dynamic Point Maps (DPM), extending standard point maps to support 4D tasks such as motion segmentation, scene flow estimation, 3D object tracking, and 2D correspondence.<n>We train a DPM predictor on a mixture of synthetic and real data and evaluate it across diverse benchmarks for video depth prediction, dynamic point cloud reconstruction, 3D scene flow and object pose tracking, achieving state-of-the-art performance.
  arXiv  Detail & Related papers  (2025-03-20T16:41:50Z)
• MonST3R: A Simple Approach for Estimating Geometry in the Presence of Motion [118.74385965694694]
  We present Motion DUSt3R (MonST3R), a novel geometry-first approach that directly estimates per-timestep geometry from dynamic scenes.<n>By simply estimating a pointmap for each timestep, we can effectively adapt DUST3R's representation, previously only used for static scenes, to dynamic scenes.<n>We show that by posing the problem as a fine-tuning task, identifying several suitable datasets, and strategically training the model on this limited data, we can surprisingly enable the model to handle dynamics.
  arXiv  Detail & Related papers  (2024-10-04T18:00:07Z)
• SMORE: Simultaneous Map and Object REconstruction [66.66729715211642]
  We present a method for dynamic surface reconstruction of large-scale urban scenes from LiDAR.<n>We take a holistic perspective and optimize a compositional model of a dynamic scene that decomposes the world into rigidly-moving objects and the background.
  arXiv  Detail & Related papers  (2024-06-19T23:53:31Z)
• 3D LiDAR Mapping in Dynamic Environments Using a 4D Implicit Neural Representation [33.92758288570465]
  Building accurate maps is a key building block to enable reliable localization, planning, and navigation of autonomous vehicles. We propose encoding the 4D scene into a novel implicit neural map representation. Our method is capable of removing the dynamic part of the input point clouds while reconstructing accurate and complete 3D maps.
  arXiv  Detail & Related papers  (2024-05-06T11:46:04Z)
• DORT: Modeling Dynamic Objects in Recurrent for Multi-Camera 3D Object Detection and Tracking [67.34803048690428]
  We propose to model Dynamic Objects in RecurrenT (DORT) to tackle this problem. DORT extracts object-wise local volumes for motion estimation that also alleviates the heavy computational burden. It is flexible and practical that can be plugged into most camera-based 3D object detectors.
  arXiv  Detail & Related papers  (2023-03-29T12:33:55Z)
• Object DGCNN: 3D Object Detection using Dynamic Graphs [32.090268859180334]
  3D object detection often involves complicated training and testing pipelines. Inspired by recent non-maximum suppression-free 2D object detection models, we propose a 3D object detection architecture on point clouds.
  arXiv  Detail & Related papers  (2021-10-13T17:59:38Z)
• ERASOR: Egocentric Ratio of Pseudo Occupancy-based Dynamic Object Removal for Static 3D Point Cloud Map Building [0.1474723404975345]
  This paper presents a novel static map building method called ERASOR, Egocentric RAtio of pSeudo Occupancy-based dynamic object Removal. Our approach directs its attention to the nature of most dynamic objects in urban environments being inevitably in contact with the ground.
  arXiv  Detail & Related papers  (2021-03-07T10:29:07Z)
• Tracking Emerges by Looking Around Static Scenes, with Neural 3D Mapping [23.456046776979903]
  We propose to leverage multiview data of textitstatic points in arbitrary scenes (static or dynamic) to learn a neural 3D mapping module. The neural 3D mapper consumes RGB-D data as input, and produces a 3D voxel grid of deep features as output. We show that our unsupervised 3D object trackers outperform prior unsupervised 2D and 2.5D trackers, and approach the accuracy of supervised trackers.
  arXiv  Detail & Related papers  (2020-08-04T02:59:23Z)

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.