GTT-Net: Learned Generalized Trajectory Triangulation

• URL: http://arxiv.org/abs/2109.03408v1
• Date: Wed, 8 Sep 2021 03:01:25 GMT
• Title: GTT-Net: Learned Generalized Trajectory Triangulation
• Abstract summary: GTT-Net is a supervised learning framework for the reconstruction of sparse dynamic 3D geometry. We show that GTT-Net outperforms the state of the art in terms of accuracy and robustness.
• Score: 26.80678903445168
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: We present GTT-Net, a supervised learning framework for the reconstruction of sparse dynamic 3D geometry. We build on a graph-theoretic formulation of the generalized trajectory triangulation problem, where non-concurrent multi-view imaging geometry is known but global image sequencing is not provided. GTT-Net learns pairwise affinities modeling the spatio-temporal relationships among our input observations and leverages them to determine 3D geometry estimates. Experiments reconstructing 3D motion-capture sequences show GTT-Net outperforms the state of the art in terms of accuracy and robustness. Within the context of articulated motion reconstruction, our proposed architecture is 1) able to learn and enforce semantic 3D motion priors for shared training and test domains, while being 2) able to generalize its performance across different training and test domains. Moreover, GTT-Net provides a computationally streamlined framework for trajectory triangulation with applications to multi-instance reconstruction and event segmentation.

Related papers

• Geometry-Grounded Unified 3D Perception for Autonomous Driving [36.11730832045922]
  Camera-based autonomous driving perception requires a shared representation that preserves metric 3D structure across synchronized multi-camera streams.<n>We present a Geometry-grounded Unified 3D Perception framework that adapts the reconstruction-oriented latent of VGGT to calibrated, streaming driving scenes.
  arXiv  Detail & Related papers  (2026-08-13T12:17:50Z)
• Pano3D: Unified 3D Reconstruction and Panoptic Segmentation [68.18993232814061]
  We introduce an approach that performs 3D reconstruction and 3D panoptic segmentation in a unified framework.<n>We build on existing 3D reconstruction models and augment them with a set-based mask decoder.<n>Our method achieves state-of-the-art performance in 3D panoptic segmentation across ScanNet, ScanNet200, and ScanNet++ datasets.
  arXiv  Detail & Related papers  (2026-06-12T09:43:13Z)
• IVGT: Implicit Visual Geometry Transformer for Neural Scene Representation [76.36174247570716]
  We propose IVGT, an Implicit Visual Geometry Transformer that implicitly models continuous and coherent geometry from pose-free multi-view images.<n> IVGT learns a continuous neural scene representation in a canonical coordinate system and supports continuous spatial queries at any 3D positions.<n>It allows direct extraction of continuous and coherent surface geometry, enabling rendering of RGB images, depth maps, and surface normal maps from arbitrary viewpoints.
  arXiv  Detail & Related papers  (2026-05-15T17:59:57Z)
• tttLRM: Test-Time Training for Long Context and Autoregressive 3D Reconstruction [47.43504457409347]
  tttLRM is a novel large 3D reconstruction model that leverages a Test-Time Training layer.<n>Our framework efficiently compresses multiple image observations into the fast weights of the TTT layer.<n>Online learning variant of our model supports progressive 3D reconstruction and refinement from streaming observations.
  arXiv  Detail & Related papers  (2026-02-23T18:59:45Z)
• Hierarchical Neural Semantic Representation for 3D Semantic Correspondence [72.8101601086805]
  We design the hierarchical neural semantic representation (HNSR), which consists of a global semantic feature to capture high-level structure and multi-resolution local geometric features.<n>Second, we design a progressive global-to-local matching strategy, which establishes coarse semantic correspondence using the global semantic feature.<n>Third, our framework is training-free and broadly compatible with various pre-trained 3D generative backbones, demonstrating strong generalization across diverse shape categories.
  arXiv  Detail & Related papers  (2025-09-22T07:23:07Z)
• TrackAny3D: Transferring Pretrained 3D Models for Category-unified 3D Point Cloud Tracking [25.788917457593673]
  TrackAny3D is the first framework to transfer large-scale pretrained 3D models for category-agnostic 3D SOT.<n>MoGE architecture adaptively activates specialized 3works based on distinct geometric characteristics.<n>Experiments show that TrackAny3D establishes new state-of-the-art performance on category-agnostic 3D SOT.
  arXiv  Detail & Related papers  (2025-07-26T10:41:55Z)
• St4RTrack: Simultaneous 4D Reconstruction and Tracking in the World [106.91539872943864]
  St4RTrack is a framework that simultaneously reconstructs and tracks dynamic video content in a world coordinate frame from RGB inputs. We predict both pointmaps at the same moment, in the same world, capturing both static and dynamic scene geometry. We establish a new extensive benchmark for world-frame reconstruction and tracking, demonstrating the effectiveness and efficiency of our unified, data-driven framework.
  arXiv  Detail & Related papers  (2025-04-17T17:55:58Z)
• 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. Specifically, our method learns an explicit matching relationship by mapping RGB pixels from both dynamic and static regions across different views to 3D pointmaps. 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)
• Large Spatial Model: End-to-end Unposed Images to Semantic 3D [79.94479633598102]
  Large Spatial Model (LSM) processes unposed RGB images directly into semantic radiance fields. LSM simultaneously estimates geometry, appearance, and semantics in a single feed-forward operation. It can generate versatile label maps by interacting with language at novel viewpoints.
  arXiv  Detail & Related papers  (2024-10-24T17:54:42Z)
• Enhancing Generalizability of Representation Learning for Data-Efficient 3D Scene Understanding [50.448520056844885]
  We propose a generative Bayesian network to produce diverse synthetic scenes with real-world patterns. A series of experiments robustly display our method's consistent superiority over existing state-of-the-art pre-training approaches.
  arXiv  Detail & Related papers  (2024-06-17T07:43:53Z)
• NEAT: Distilling 3D Wireframes from Neural Attraction Fields [52.90572335390092]
  This paper studies the problem of structured lineframe junctions using 3D reconstruction segments andFocusing junctions. ProjectNEAT enjoys the joint neural fields and view without crossart matching from scratch.
  arXiv  Detail & Related papers  (2023-07-14T07:25:47Z)
• SeMLaPS: Real-time Semantic Mapping with Latent Prior Networks and Quasi-Planar Segmentation [53.83313235792596]
  We present a new methodology for real-time semantic mapping from RGB-D sequences. It combines a 2D neural network and a 3D network based on a SLAM system with 3D occupancy mapping. Our system achieves state-of-the-art semantic mapping quality within 2D-3D networks-based systems.
  arXiv  Detail & Related papers  (2023-06-28T22:36:44Z)
• Cross-Dimensional Refined Learning for Real-Time 3D Visual Perception from Monocular Video [2.2299983745857896]
  We present a novel real-time capable learning method that jointly perceives a 3D scene's geometry structure and semantic labels. We propose an end-to-end cross-dimensional refinement neural network (CDRNet) to extract both 3D mesh and 3D semantic labeling in real time.
  arXiv  Detail & Related papers  (2023-03-16T11:53:29Z)
• GraphCSPN: Geometry-Aware Depth Completion via Dynamic GCNs [49.55919802779889]
  We propose a Graph Convolution based Spatial Propagation Network (GraphCSPN) as a general approach for depth completion. In this work, we leverage convolution neural networks as well as graph neural networks in a complementary way for geometric representation learning. Our method achieves the state-of-the-art performance, especially when compared in the case of using only a few propagation steps.
  arXiv  Detail & Related papers  (2022-10-19T17:56:03Z)
• Single-view 3D Mesh Reconstruction for Seen and Unseen Categories [69.29406107513621]
  Single-view 3D Mesh Reconstruction is a fundamental computer vision task that aims at recovering 3D shapes from single-view RGB images. This paper tackles Single-view 3D Mesh Reconstruction, to study the model generalization on unseen categories. We propose an end-to-end two-stage network, GenMesh, to break the category boundaries in reconstruction.
  arXiv  Detail & Related papers  (2022-08-04T14:13:35Z)
• Learning monocular 3D reconstruction of articulated categories from motion [39.811816510186475]
  Video self-supervision forces the consistency of consecutive 3D reconstructions by a motion-based cycle loss. We introduce an interpretable model of 3D template deformations that controls a 3D surface through the displacement of a small number of local, learnable handles. We obtain state-of-the-art reconstructions with diverse shapes, viewpoints and textures for multiple articulated object categories.
  arXiv  Detail & Related papers  (2021-03-30T13:50:27Z)
• Monocular 3D Detection with Geometric Constraints Embedding and Semi-supervised Training [3.8073142980733]
  We propose a novel framework for monocular 3D objects detection using only RGB images, called KM3D-Net. We design a fully convolutional model to predict object keypoints, dimension, and orientation, and then combine these estimations with perspective geometry constraints to compute position attribute.
  arXiv  Detail & Related papers  (2020-09-02T00:51:51Z)
• Joint Spatial-Temporal Optimization for Stereo 3D Object Tracking [34.40019455462043]
  We propose a joint spatial-temporal optimization-based stereo 3D object tracking method. From the network, we detect corresponding 2D bounding boxes on adjacent images and regress an initial 3D bounding box. Dense object cues that associating to the object centroid are then predicted using a region-based network.
  arXiv  Detail & Related papers  (2020-04-20T13:59:46Z)

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.