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20182026
most citedReal-time Monte Carlo Denoising with Weight Sharing Kernel Prediction Network

27 citations · 69 across the 34 of their papers we have counts for

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Showing 2023Show all

6 papers · 1 filter

cs.CV2023

Holistic Inverse Rendering of Complex Facade via Aerial 3D Scanning

Zixuan Xie, Rengan Xie, Rong Li +8

In this work, we use multi-view aerial images to reconstruct the geometry, lighting, and material of facades using neural signed distance fields (SDFs). Without the requirement of…

cs.GR2023★ 1 cited

FuseSR: Super Resolution for Real-time Rendering through Efficient Multi-resolution Fusion

Zhihua Zhong, Jingsen Zhu, Yuxin Dai +5

The workload of real-time rendering is steeply increasing as the demand for high resolution, high refresh rates, and high realism rises, overwhelming most graphics cards. To mitiga…

cs.AI2023

Topological RANSAC for instance verification and retrieval without fine-tuning

Guoyuan An, Juhyung Seon, Inkyu An +2

This paper presents an innovative approach to enhancing explainable image retrieval, particularly in situations where a fine-tuning set is unavailable. The widely-used SPatial veri…

cs.CV2023

Towards Content-based Pixel Retrieval in Revisited Oxford and Paris

Guoyuan An, Woo Jae Kim, Saelyne Yang +3

This paper introduces the first two pixel retrieval benchmarks. Pixel retrieval is segmented instance retrieval. Like semantic segmentation extends classification to the pixel leve…

cs.CV2023★ 4 cited

Seal-3D: Interactive Pixel-Level Editing for Neural Radiance Fields

Xiangyu Wang, Jingsen Zhu, Qi Ye +4

With the popularity of implicit neural representations, or neural radiance fields (NeRF), there is a pressing need for editing methods to interact with the implicit 3D models for t…

cs.CV2023★ 1 cited

I-SDF: Intrinsic Indoor Scene Reconstruction and Editing via Raytracing in Neural SDFs

Jingsen Zhu, Yuchi Huo, Qi Ye +8

In this work, we present I-SDF, a new method for intrinsic indoor scene reconstruction and editing using differentiable Monte Carlo raytracing on neural signed distance fields…