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20182026
most citedAn efficient solution for semantic segmentation: ShuffleNet V2 with atrous separable convolutions

23 citations · 33 across the 24 of their papers we have counts for

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26 papers · 1 filter

cs.CV2026

OP2GS: Object-Aware 3D Gaussian Splatting with Dual-Opacity Primitives

Guiyu Liu, Niklas Vaara, Janne Mustaniemi +2

3D Gaussian Splatting (3DGS) provides an explicit and efficient scene representation, but its primitives lack inherent object-level identity, hindering downstream tasks such as ope…

cs.CV2026

Differentiable Ray Tracing with Gaussians for Unified Radio Propagation Simulation and View Synthesis

Niklas Vaara, Lam Huynh, Pekka Sangi +2

Explicit neural representations such as 3D Gaussian Splatting (3DGS) enable high-fidelity and real-time novel view synthesis, yet optimize for alpha-composited optical appearance r…

cs.CV2026

Scene-Agnostic Object-Centric Representation Learning for 3D Gaussian Splatting

Tsuheng Hsu, Guiyu Liu, Juho Kannala +1

Recent works on 3D scene understanding leverage 2D masks from visual foundation models (VFMs) to supervise radiance fields, enabling instance-level 3D segmentation. However, the su…

cs.CV2025

Towards an Automated Multimodal Approach for Video Summarization: Building a Bridge Between Text, Audio and Facial Cue-Based Summarization

Md Moinul Islam, Sofoklis Kakouros, Janne Heikkilä +1

The increasing volume of video content in educational, professional, and social domains necessitates effective summarization techniques that go beyond traditional unimodal approach…

cs.CV2024★ 3 cited

GS-Pose: Generalizable Segmentation-based 6D Object Pose Estimation with 3D Gaussian Splatting

Dingding Cai, Janne Heikkilä, Esa Rahtu

This paper introduces GS-Pose, a unified framework for localizing and estimating the 6D pose of novel objects. GS-Pose begins with a set of posed RGB images of a previously unseen…

cs.CV2023

DiverseDream: Diverse Text-to-3D Synthesis with Augmented Text Embedding

Uy Dieu Tran, Minh Luu, Phong Ha Nguyen +2

Text-to-3D synthesis has recently emerged as a new approach to sampling 3D models by adopting pretrained text-to-image models as guiding visual priors. An intriguing but underexplo…