Recent Papers in 3D Computer Vision

Some research works related to 3D computer vision, published in the recent top venues. Lists and details to be extended…

ECCV 2016

Structure-from-Motion and Pose Estimation:

As you have noticed, this is my paper :). We propose a new method of matching image collections for SfM, which improves the matching efficiency as well as coping with ambiguous scenes. Leave a comment (at the bottom of this page) if you are interested.

  • Je Hyeong Hong, Christopher Zach, Andrew Fitzgibbon, Roberto Cipolla. Projective Bundle Adjustment from Arbitrary Initialization using the Variable Projection Method.

  • Jonathan Ventura. Structure from Motion on a Sphere.

  • Gaku Nakano. A Versatile Approach for Solving PnP, PnPf, and PnPfr Problems.

  • Cenek Albl, Akihiro Sugimoto, Tomas Pajdla, Degeneracies in Rolling Shutter SfM.

  • Gim Hee Lee. A Minimal Solution for Non-Perspective Pose Estimation from Line Correspondences.

  • Federico Camposeco, Torsten Sattler, Marc Pollefeys, Minimal Solvers for Generalized Pose and Scale Estimation from Two Rays and One Point.

  • Pose Estimation Errors, the Ultimate Diagnosis.

  • Kyle Wilson, David Bindel, Noah Snavely. When is Rotations Averaging Hard?

  • ShapeFit and ShapeKick for Robust, Scalable Structure from Motion.

Another translation averaging method based on ADMM.

  • Branching Gaussian Processes with Applications to Spatiotemporal Reconstruction of 3D Trees

  • Accurate and Linear Time Pose Estimation from Points and Lines.

  • Robust and Accurate Line- and/or Point-Based Pose Estimation without Manhattan Assumptions.

  • \piMatch: Monocular vSLAM and Piecewise Planar Reconstruction using Fast Plane Correspondences

  • Bayesian Image based 3D Pose Estimation


This is kind of a promotion for my friend and labmate Shiwei’s work. They have done excellent work on multi-view stereo which powers the 3D reconstruction engine of Altizure, take a look the paper if you are interested:).

The results are quite impressive, including a video for demonstration.

  • Fabian Langguth, Kalyan Sunkavalli, Sunil Hadap, Michael Goesele. Shading-aware Multi-view Stereo.



  • Indoor-Outdoor 3D Reconstruction Alignment

  • Minglei Li, Peter Wonka, Liangliang Nan. Manhattan-world Urban Reconstruction from Point Clouds.

  • Lama Affara, Liangliang Nan, Bernard Ghanem, Peter Wonka. Large Scale Asset Extraction for Urban Images.

The information for the above two papers can be accessed here.

  • Federica Arrigoni, Beatrice Rossi, Andrea Fusiello. Global Registration of 3D Point Sets via LRS decomposition.

  • Liuyun Duan, Florent Lafarge. Towards large-scale city reconstruction from satellites.

  • Hanme Kim, Stefan Leutenegger, Andrew Davison. Real-Time 3D Reconstruction and 6-DoF Tracking with an Event Camera.

  • Template-free 3D Reconstruction of Poorly-textured Nonrigid Surfaces

  • Real-time Large-Scale Dense 3D Reconstruction with Loop Closure.

  • 3D-R2N2: A unified approach for single and multi-view 3D object reconstruction.

Feature and Matching:

This is a very interesting work that obtains local feature with deep neural nets. I am looking forward to the code.

  • Nam Vo, James Hays. Localizing and Orienting Street Views Using Overhead Imagery.

Matching ground-level image with overhead imagery using CNN

  • Yoni Kasten, Gil Ben-Artzi, Shmuel Peleg, Michael Werman. Fundamental Matrices from Moving Objects Using Line Motion Barcodes.

  • Wen-Yan Lin, Siying Liu, Nianjuan Jiang, Minh Do, Ping Tan, Jiangbo Lu. RepMatch: Robust Feature Matching and Pose for Reconstructing Modern Cities.

  • Amir R. Zamir, Pulkit Agrawal, Tilman Wekel, Jitendra Malik, Silvio Savarese. Generic 3D Representations via Pose Estimation and Matching. (DL)


  • Avi Kaplan, Tamar Avraham, Michael Lindenbaum. Interpreting the Ratio Criterion for Matching SIFT Descriptors,

  • Guacn Long, Laurent Kneip, Jose M. Alvarez, Hongdong Li, Xiaohu Zhang, Qifeng Yu. Learning Image Matching by Simply Watching Video.

  • Guided Matching based on Statistical Optical Flow for Fast and Robust Correspondence Analysis.

Image Retrieval:

  • Filip Radenovic, Giorgos Tolias, Ondra Chum. CNN Image Retrieval Learns from BoW: Unsupervised Fine-Tuning with Hard Examples (oral)

  • Xiaohan Fei, Konstantine Tsotsos, Stefano Soatto. A Simple Hierarchical Pooling Data Structure for Loop Closure.

  • Albert Gordo, Jon Almazan, Jerome Revaud, Diane Larlus. Deep Image Retrieval: Learning Global Representations for Image Search.

  • Kernel-Based Supervised Discrete Hashing for Image Retrieval

CVPR 2016


  • ***Vo, M., Narasimhan, S. G., & Sheikh, Y. Spatiotemporal Bundle Adjustment for Dynamic 3D Reconstruction.

Project website, 强烈推荐video,演员十分癫狂…

  • Hao Wang, Jun Wang, Wang Liang. Online Reconstruction of Indoor Scenes From RGB-D Streams.

  • Ali Osman Ulusoy, Michael J. Black, Andreas Geiger. Patches, Planes and Probabilities: A Non-Local Prior for Volumetric 3D Reconstruction.

  • Olivier Saurer, Marc Pollefeys, Gim Hee Lee. Sparse to Dense 3D Reconstruction From Rolling Shutter Images.

  • **Filip Radenovic, Johannes L. Schönberger, Dinhuang Ji, Jan-Michael Frahm, Ondrej Chum, Jiri Matas. From Dusk till Dawn: Modeling in the Dark

A method to cope with illumination changes in Internet dataset. After camera registration, a clustering method is applied and seperate the scene graph into the Day cluster and the Night cluster. Dense models are reconstruction seperately, following a fusion process. (sparse -> dense)

  • Fan, Bin, et al. “Do We Need Binary Features for 3D Reconstruction?.”

This paper discusses whether it is necessary to use binary features in 3D reconstruction.


  • ***Johannes L. Schönberger, Jan-Michael Frahm. Structure-From-Motion Revisited.


Feature and Matching:

Project website: worth trying since there is code available.

  • Swarna K. Ravindran, Anurag Mittal. CoMaL - Good Features to Match on Object Boundaries.

  • Yuan-Ting Hu, Yen-Yu Lin. Progressive Feature Matching With Alternate Descriptor Selection and Correspondence Enrichment.

  • Angjoo Kanazawa, David W. Jacobs, Manmohan Chandraker. WarpNet: Weakly Supervised Matching for Single-View Reconstruction. (DL)

  • Jin Xie, Meng Wang, Yi Fang. Learned Binary Spectral Shape Descriptor for 3D Shape Correspondence.

  • Zhou, Tinghui, et al. “Learning Dense Correspondence via 3D-guided Cycle Consistency.” (DL)

Project page


  • Haomiao Liu, Ruiping Wang, Shiguang Shan, Xilin Chen. Deep Supervised Hashing for Fast Image Retrieval. (DL)

  • Eng-Jon Ong, Miroslaw Bober. Improved Hamming Distance Search Using Variable Length Substrings.

  • Jae-Pil Heo, Zhe Lin, Xiaohui Shen, Jonathan Brandt, Sung-eui Yoon. Shortlist Selection With Residual-Aware Distance Estimator for K-Nearest Neighbor Search.

  • Xiaojuan Wang, Ting Zhang, Guo-Jun Qi, Jinhui Tang, Jingdong Wang. Supervised Quantization for Similarity Search .

  • Patrick Wieschollek, Oliver Wang, Alexander Sorkine-Hornung, Hendrik P. A. Lensch. Efficient Large-Scale Approximate Nearest Neighbor Search on the GPU.

  • Ting Zhang, Jingdong Wang. Collaborative Quantization for Cross-Modal Similarity Search.

  • Thi Quynh Nhi Tran, Hervé Le Borgne, Michel Crucianu. Aggregating Image and Text Quantized Correlated Components.

  • Artem Babenko, Victor Lempitsky. Efficient Indexing of Billion-Scale Datasets of Deep Descriptors.

  • ***Ahmet Iscen, Michael Rabbat, Teddy Furon. Efficient Large-Scale Similarity Search Using Matrix Factorization.

  • Theodora Kontogianni, Markus Mathias, Bastian Leibe. Incremental Object Discovery in Time-Varying Image Collections.

  • Torsten Sattler, Michal Havlena, Konrad Schindler, Marc Pollefeys. Large-Scale Location Recognition and the Geometric Burstiness Problem.



  • Alex Locher, Michal Perdoch, Luc Van Gool. Progressive Prioritized Multi-View Stereo.


  • Cédric Verleysen, Christophe De Vleeschouwer. Piecewise-Planar 3D Approximation From Wide-Baseline Stereo.

  • John Flynn, Ivan Neulander, James Philbin, Noah Snavely. DeepStereo: Learning to Predict New Views From the World’s Imagery.

Accepted in CVPR 2016 but released a year ago (2015), see the video

Segmentation and Scene Understanding:

  • Ole Johannsen, Antonin Sulc, Bastian Goldluecke. What Sparse Light Field Coding Reveals About Scene Structure.


  • Ian Schillebeeckx, Robert Pless. Single Image Camera Calibration With Lenticular Arrays for Augmented Reality.

  • Andrey Bushnevskiy, Lorenzo Sorgi, Bodo Rosenhahn. Multicamera Calibration From Visible and Mirrored Epipoles.

Pose, Rolling Shutter & Other:

  • Eric Brachmann, Frank Michel, Alexander Krull, Michael Ying Yang, Stefan Gumhold, Carsten Rother. Uncertainty-Driven 6D Pose Estimation of Objects and Scenes From a Single RGB Image.

  • Cenek Albl, Zuzana Kukelova, Tomas Pajdla. Rolling Shutter Absolute Pose Problem With Known Vertical Direction.

  • Optimal Relative Pose with Unknown Correspondences

  • Compute epipolar geometry and correspondences at the same time, theoretically interesting.

  • Luca Magri, Andrea Fusiello. Multiple Model Fitting as a Set Coverage Problem.

  • Matthew Trager, Martial Hebert, Jean Ponce. Consistency of Silhouettes and Their Duals.

ICCV 2015

Tracking and Localization:

  • Joseph Tan, D., Tombari, F., Ilic, S., & Navab, N. (2015). A Versatile Learning-Based 3D Temporal Tracker: Scalable, Robust, Online.

A tracking algorithm using depth images


  • Diamond, S., & Boyd, S. (2015). Convex Optimization With Abstract Linear Operators.

CVX, Cone programming, linear transform

SfM and Visual SLAM

  • Jose Tarrio, J., & Pedre, S. (2015). Realtime Edge-Based Visual Odometry for a Monocular Camera.

Edge feature based visual odometry with code

  • Johannsen, O., Sulc, A., & Goldluecke, B. (2015). On Linear Structure from Motion for Light Field Cameras.

An application of Lytro cinema.

  • Cui, Zhaopeng, and Ping Tan. “Global Structure-from-Motion by Similarity Averaging.”

Yet another global method for SfM


  • Benjamin Ummenhofer and Thomas Brox. Global, Dense Multiscale Reconstruction for a Billion Points.

Multi-scale surface reconstruction. Video


  • Martin-Brualla, R., Gallup, D., & Seitz, S. M. (2015). 3D Time-Lapse Reconstruction from Internet Photos.

Given an Internet photo collection of a landmark, we compute a 3D time-lapse video sequence where a virtual camera moves continuously in time and space. Project website

  • Ikehata, S., Yang, H., & Furukawa, Y. (2015). Structured Indoor Modeling.

Project website, with matlab code and datasets.

  • Zheng, Enliang, et al. Minimal Solvers for 3D Geometry from Satellite Imagery.

Pose, Point Cloud & Others

  • Katz, S., & Tal, A. (2015). On the Visibility of Point Clouds.

determine the visible subset of points directly from a given point cloud


CVPR 2015

SfM and Localization:

  • Lin, Tsung-Yi, et al. “Learning deep representations for ground-to-aerial geolocalization.”

  • Song, S., & Chandraker, M. (2015). Joint SFM and detection cues for monocular 3D localization in road scenes.


  • **Choi, Sungjoon, Qian-Yi Zhou, and Vladlen Koltun. “Robust reconstruction of indoor scenes.”

Indoor scene reconstruction from RGB-D video, with code and dataset available.


  • Savinov, Nikolay, Christian Hane, and Marc Pollefeys. “Discrete optimization of ray potentials for semantic 3D reconstruction.”

  • Jung, J., Lee, J. Y., & Kweon, I. S. (2015, June). One-day outdoor photometric stereo via skylight estimation.

  • Xie, W., Dai, C., & Wang, C. C. (2015, June). Photometric stereo with near point lighting: A solution by mesh deformation.

  • Li, Zhuwen, et al. “Simultaneous video defogging and stereo reconstruction.”

Feature and Matching:

  • *Litman, Roee, et al. “Inverting RANSAC: Global Model Detection via Inlier Rate Estimation.”

  • Dong, Jingming, and Stefano Soatto. “Domain-size pooling in local descriptors: DSP-SIFT.”

  • **Yumin Suh, Kamil Adamczewski, Kyoung Mu Lee. “Subgraph Matching Using Compactness Prior for Robust Feature Correspondence”

  • Yanchao Yang, Zhaojin Lu, Ganesh Sundaramoorthi. “Coarse-To-Fine Region Selection and Matching”

  • Faraki, Masoud, Mehrtash T. Harandi, and Fatih Porikli. “More About VLAD: A Leap from Euclidean to Riemannian Manifolds.”


  • ***Li, Xinchao, Martha Larson, and Alan Hanjalic. “Pairwise geometric matching for large-scale object retrieval.”

  • Jiang, Ke, Qichao Que, and Brian Kulis. “Revisiting kernelized locality-sensitive hashing for improved large-scale image retrieval.”

  • **Johnson, Justin, et al. “Image retrieval using scene graphs.” Computer Vision and Pattern Recognition (CVPR), 2015.

sementic image retrieval

3D with Sensors:

  • Ye, M., Zhang, Y., Yang, R., & Manocha, D. “3d reconstruction in the presence of glasses by acoustic and stereo fusion.”

  • Gupta, S., Arbeláez, P., Girshick, R., & Malik, J. “Aligning 3D models to RGB-D images of cluttered scenes.”

Segmentation and Scene Understanding:

  • Wang, S., Fidler, S., & Urtasun, R. (2015, June). Holistic 3d scene understanding from a single geo-tagged image.

  • Martinovic, Andelo, et al. “3d all the way: Semantic segmentation of urban scenes from start to end in 3d.”

Written on May 20, 2016