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arXiv Computer Vision

Partition-Invariant Tuning for 3D Scene Understanding

The paper introduces PointPiT, a partition‑invariant tuning framework designed for scene‑level point cloud understanding. It combines a Scene‑aware Structural Adapter (SSA) that fuses local geometry with global context, and Gradient Subspace Optimization (GSO) that selects stable update directions to reduce partition‑induced representation shifts. Experiments on multiple benchmarks show that PointPiT matches or surpasses full fine‑tuning while using less than 1% of the backbone’s parameters, achieving state‑of‑the‑art performance among parameter‑efficient fine‑tuning methods.

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arXiv Computer Vision
Sep 3

PointGauss: Point Cloud-Guided Multi-Object Segmentation for Gaussian Splatting

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.

By Wentao Sun, Yiping Chen, John S. Zelek, Jonathan Li
llmscomputer-visionefficiencybenchmarks
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arXiv AI
Sep 7

Mitigating Performance Discrepancy in Cross-Domain 3D Class-Incremental Learning

The paper addresses performance discrepancy in cross-domain 3D class‑incremental learning, where 3D point clouds from heterogeneous sources cause varying degrees of performance loss beyond catastrophic forgetting. The authors introduce the Domain3D‑CIL protocol and adapt existing CIL methods to 3D, showing consistent discrepancy across baselines. They propose PolyMem, an exemplar‑free approach that models high‑order feature statistics to improve cross‑domain robustness and reduce performance discrepancy.

By Jinge Ma, Gautham Vinod, Bruce Coburn, Jui-Feng Chi, Siddeshwar Raghavan, Fengqing Zhu
agentsrobotics
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Hugging Face Trending Papers
Jul 9

Geometry and Gradient-based Partitioning for Panoramic Outdoor Reconstruction

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.

benchmarks
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