arXiv Computer Vision August 31, 2026 By Guanglin Jin, Hongshan Yu, Javier Civera, Zhaoxin Li

ZipMVS: Multi-View Stereo with Compressed Cost Volumes

Read the original on arXiv Computer Vision →

ZipMVS is a multi‑view stereo (MVS) approach that focuses on reducing memory usage while maintaining high‑quality 3D reconstructions. It introduces a new depth‑hypothesis strategy that compresses the cost volume, significantly lowering GPU memory consumption. Experiments on the DTU and Tanks and Temples datasets demonstrate that ZipMVS delivers competitive reconstruction quality compared to other efficiency‑oriented MVS methods.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Computer Vision.

agents

Hugging Face Trending Papers

Jun 29

StereoGS: Sparse-View 3D Gaussian Splatting via Stereo Priors

3D Gaussian Splatting (3DGS) has achieved remarkable success in real-time novel view synthesis, yet it suffers from severe overfitting under sparse-view settings due to insufficient geometric constraints. While recent methods introduce monocular depth priors to mitigate this, they inherently struggle with scale ambiguity and cross-view inconsistency, leading to defective geometry.

computer-vision benchmarks

arXiv Machine Learning

Jun 25

SparseGS: Sparse View Synthesis using 3D Gaussian Splatting

arXiv:2312. 00206v4 Announce Type: replace-cross Abstract: 3D Gaussian Splatting (3DGS) has recently enabled real-time rendering of unbounded 3D scenes for novel view synthesis.

By Haolin Xiong, Sairisheek Muttukuru, Hanyuan Xiao, Rishi Upadhyay, Pradyumna Chari, Yajie Zhao, Achuta Kadambi

efficiency

arXiv Computer Vision

Aug 24

M2Depth: Unifying Monocular Depth Foundation Priors with Multi-View Stereo

arXiv:2608.20788v1 Announce Type: new Abstract: Deep learning-based Multi-View Stereo (MVS) has advanced significantly but often generalizes poorly to unseen scenes, particularly in occluded areas or...

By Byeonggwon Lee, Sanggi Lee, Siwoo Lee, Khang Truong Giang, Soohwan Song

benchmarks

arXiv Machine Learning

Jul 2

Efficient Compression of Structured and Unstructured Volumes via Learned 3D Gaussian Representation

arXiv:2607. 01164v1 Announce Type: new Abstract: Recent work has shown that implicit neural representations (INRs) can be trained to effectively compress structured and unstructured volume data, allowing for direct data querying with a reduced memory footprint.

By Landon Dyken, Sharmistha Chakrabarti, Nathan Debardeleben, Steve Petruzza, Qi Wu, Will Usher, Sidharth Kumar

efficiency

arXiv Computer Vision

3d ago

NBS: No Bias Stereo

arXiv:2608.28933v1 Announce Type: new Abstract: Stereo reconstruction is one of the last remaining Computer Vision tasks where all state-of-the-art methods employ a heavy architectural inductive bias...

By Vage Taamazyan, Zhuowen Shen, Stefan Hinterstoisser, Alberto Dall'Olio, Agastya Kalra, Aarrushi Shandilya, Xin Li, Wenping Wang, Kartik Venkataraman

llms computer-vision benchmarks safety

arXiv AI

Jul 10

LSRM: High-Fidelity Object-Centric Reconstruction via Scaled Context Windows

arXiv:2604. 05182v2 Announce Type: replace-cross Abstract: We introduce the Large Sparse Reconstruction Model to study how scaling transformer context windows affects feed-forward 3D reconstruction.

By Zhengqin Li, Cheng Zhang, Jakob Engel, Zhao Dong

llms benchmarks