LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning

• URL: http://arxiv.org/abs/2504.18424v1
• Date: Fri, 25 Apr 2025 15:31:29 GMT
• Title: LaRI: Layered Ray Intersections for Single-view 3D Geometric Reasoning
• Authors: Rui Li, Biao Zhang, Zhenyu Li, Federico Tombari, Peter Wonka,
• Abstract summary: layered ray intersections (LaRI) is a new method for unseen geometry reasoning from a single image.<n>Benefiting from the compact and layered representation, LaRI enables complete, efficient, and view-aligned geometric reasoning.<n>We build a complete training data generation pipeline for synthetic and real-world data, including 3D objects and scenes.
• Score: 75.9814389360821
• License: http://creativecommons.org/licenses/by-nc-sa/4.0/
• Abstract: We present layered ray intersections (LaRI), a new method for unseen geometry reasoning from a single image. Unlike conventional depth estimation that is limited to the visible surface, LaRI models multiple surfaces intersected by the camera rays using layered point maps. Benefiting from the compact and layered representation, LaRI enables complete, efficient, and view-aligned geometric reasoning to unify object- and scene-level tasks. We further propose to predict the ray stopping index, which identifies valid intersecting pixels and layers from LaRI's output. We build a complete training data generation pipeline for synthetic and real-world data, including 3D objects and scenes, with necessary data cleaning steps and coordination between rendering engines. As a generic method, LaRI's performance is validated in two scenarios: It yields comparable object-level results to the recent large generative model using 4% of its training data and 17% of its parameters. Meanwhile, it achieves scene-level occluded geometry reasoning in only one feed-forward.

Related papers

• GeoLRM: Geometry-Aware Large Reconstruction Model for High-Quality 3D Gaussian Generation [65.33726478659304]
  We introduce the Geometry-Aware Large Reconstruction Model (GeoLRM), an approach which can predict high-quality assets with 512k Gaussians and 21 input images in only 11 GB GPU memory. Previous works neglect the inherent sparsity of 3D structure and do not utilize explicit geometric relationships between 3D and 2D images. GeoLRM tackles these issues by incorporating a novel 3D-aware transformer structure that directly processes 3D points and uses deformable cross-attention mechanisms.
  arXiv  Detail & Related papers  (2024-06-21T17:49:31Z)
• GeoGen: Geometry-Aware Generative Modeling via Signed Distance Functions [22.077366472693395]
  We introduce a new generative approach for synthesizing 3D geometry and images from single-view collections. By employing volumetric rendering using neural radiance fields, they inherit a key limitation: the generated geometry is noisy and unconstrained. We propose GeoGen, a new SDF-based 3D generative model trained in an end-to-end manner.
  arXiv  Detail & Related papers  (2024-06-06T17:00:10Z)
• X-Ray: A Sequential 3D Representation For Generation [54.160173837582796]
  We introduce X-Ray, a novel 3D sequential representation inspired by x-ray scans. X-Ray transforms a 3D object into a series of surface frames at different layers, making it suitable for generating 3D models from images.
  arXiv  Detail & Related papers  (2024-04-22T16:40:11Z)
• RayDF: Neural Ray-surface Distance Fields with Multi-view Consistency [10.55497978011315]
  We propose a new framework called RayDF to formulate 3D shapes as ray-based neural functions. Our method achieves a 1000x faster speed than coordinate-based methods to render an 800x800 depth image.
  arXiv  Detail & Related papers  (2023-10-30T15:22:50Z)
• Implicit Ray-Transformers for Multi-view Remote Sensing Image Segmentation [26.726658200149544]
  We propose ''Implicit Ray-Transformer (IRT)'' based on Implicit Neural Representation (INR) for RS scene semantic segmentation with sparse labels. The proposed method includes a two-stage learning process. In the first stage, we optimize a neural field to encode the color and 3D structure of the remote sensing scene. In the second stage, we design a Ray Transformer to leverage the relations between the neural field 3D features and 2D texture features for learning better semantic representations.
  arXiv  Detail & Related papers  (2023-03-15T07:05:07Z)
• Explicit3D: Graph Network with Spatial Inference for Single Image 3D Object Detection [35.85544715234846]
  We propose a dynamic sparse graph pipeline named Explicit3D based on object geometry and semantics features. Our experimental results on the SUN RGB-D dataset demonstrate that our Explicit3D achieves better performance balance than the-state-of-the-art.
  arXiv  Detail & Related papers  (2023-02-13T16:19:54Z)
• DETR4D: Direct Multi-View 3D Object Detection with Sparse Attention [50.11672196146829]
  3D object detection with surround-view images is an essential task for autonomous driving. We propose DETR4D, a Transformer-based framework that explores sparse attention and direct feature query for 3D object detection in multi-view images.
  arXiv  Detail & Related papers  (2022-12-15T14:18:47Z)
• Normal Transformer: Extracting Surface Geometry from LiDAR Points Enhanced by Visual Semantics [7.507853813361308]
  We introduce a multi-modal technique that leverages 3D point clouds and 2D colour images obtained from LiDAR and camera sensors for surface normal estimation. We present a novel transformer-based neural network architecture that proficiently fuses visual semantic and 3D geometric information. It has been verified that the proposed model can learn from a simulated 3D environment that mimics a traffic scene.
  arXiv  Detail & Related papers  (2022-11-19T03:55:09Z)
• Unsupervised Multi-View Object Segmentation Using Radiance Field Propagation [55.9577535403381]
  We present a novel approach to segmenting objects in 3D during reconstruction given only unlabeled multi-view images of a scene. The core of our method is a novel propagation strategy for individual objects' radiance fields with a bidirectional photometric loss. To the best of our knowledge, RFP is the first unsupervised approach for tackling 3D scene object segmentation for neural radiance field (NeRF)
  arXiv  Detail & Related papers  (2022-10-02T11:14:23Z)
• Refer-it-in-RGBD: A Bottom-up Approach for 3D Visual Grounding in RGBD Images [69.5662419067878]
  Grounding referring expressions in RGBD image has been an emerging field. We present a novel task of 3D visual grounding in single-view RGBD image where the referred objects are often only partially scanned due to occlusion. Our approach first fuses the language and the visual features at the bottom level to generate a heatmap that localizes the relevant regions in the RGBD image. Then our approach conducts an adaptive feature learning based on the heatmap and performs the object-level matching with another visio-linguistic fusion to finally ground the referred object.
  arXiv  Detail & Related papers  (2021-03-14T11:18:50Z)

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.