arXiv:2603.06168v4 Announce Type: replace Abstract: Semantic segmentation across visual modalities such as 3D point clouds and panoramic images remains a challenging task, primarily due to the scarci...
arXiv:2611.05874v1 Announce Type: new Abstract: Humans can easily recognize the same objects across different viewpoints of a 3D scene, whereas teaching models such multi-view-consistent perception t...
arXiv:2608.21136v1 Announce Type: new Abstract: Recently, open-vocabulary zero-shot 3D scene understanding using vision foundation models has emerged as a promising alternative to data-intensive supe...
arXiv:2607. 00832v1 Announce Type: cross Abstract: A single panorama captures the full visual sphere from one camera center, yet confines users to looking around in place without enabling true scene exploration.
Scaling 3D Gaussian Splatting (3DGS) to large outdoor scenes is costly in both data acquisition and computation. Adopting panoramic images with equirectangular projection (ERP) can reduce capture effort via their full $360^{\circ}$ field of view, yet the resulting omnipresent visibility invalidates existing partitioning strategies that rely on local camera frustums, causing block-wise optimization to degenerate into global training.
GeoPano is a feed‑forward framework that reconstructs geometrically accurate 3D point clouds from a single panoramic image. It introduces a panoramic‑aware attention aggregator with a novel Pano‑Attention mechanism to exploit spherical geometry and reduce cross‑view inconsistencies. A hybrid supervision strategy enforces both fine‑grained local detail in perspective views and global structural coherence in spherical space, leading to superior reconstruction accuracy, consistency, and visual fidelity compared to prior state‑of‑the‑art methods.
The paper introduces PLOVIS, a data‑efficient framework for semantic segmentation of 3D point clouds that tackles three simultaneous data shortages: few training scenes, sparse point‑level labels, and missing 2D image sequences. PLOVIS generates pseudo‑labels by applying an open‑vocabulary image segmentation model directly to 3D point clouds, then refines these labels through a two‑stage filtering process and a class‑balanced memory bank. Experiments on ScanNet, S3DIS, Toronto3D, and Semantic3D show that PLOVIS outperforms existing fine‑tuning and weakly supervised methods under realistic, data‑scarce conditions.
arXiv:2605.13018v2 Announce Type: replace Abstract: Object-centric scene understanding is a fundamental challenge in computer vision. Existing approaches often rely on multi-stage pipelines that firs...
The paper introduces RelationVGGT, a feed‑forward framework that performs 3D spatial relation segmentation without per‑scene optimization or known camera poses. It combines semantic features from a visual foundation model with geometry‑aware representations from a 3D geometry foundation model, and uses a relation transformer to predict subject‑conditioned, cross‑view relations based on a visual subject and a textual query. The authors also present an automated annotation pipeline built on ScanNet++ with VLMs and LLMs to generate scalable training data for this new task.
arXiv:2404.09431v3 Announce Type: replace Abstract: Pseudo-LiDAR has become a promising paradigm for monocular 3D object detection by transforming monocular images into point cloud representations th...
arXiv:2512.22819v2 Announce Type: replace Abstract: Panoramic depth estimation captures the complete 360$^\circ$ scene geometry, being essential for robotics and AR/VR applications. While perspective...
PointGauss is a 3D-native framework that performs semantic parsing and instance segmentation on 3D Gaussian splatting representations by treating Gaussian primitives as unstructured point sets and extracting scale‑invariant geometric features with Point Transformer V3. It introduces an adaptive region‑of‑interest cropping strategy and an instance‑aware distance‑constrained rasterization pipeline to enable scalable, view‑consistent pixel‑level projections. The authors also release SplatSeg‑360, a cross‑scale benchmark with 32 complex scenes and over 6,300 aligned 2D‑3D masks, and show that PointGauss achieves real‑time performance with state‑of‑the‑art 3D‑mIoU (~90%) and 2D‑mIoU (~80%) scores.