Fetching the paper…
Reading the bibliography…
The choice of scene representation is crucial in both the shape inference algorithms it requires and the smart applications it enables.
Ray tracing volume densities, 1984
J. T. Kajiya and B. P. Von Herzen · 1984
Earlier work this paper cites.
MonoSLAM: Real-Time Single Camera SLAM
A. J. Davison, N. D. Molton, I. Reid, and O. Stasse · 2007
Earlier work this paper cites.
Parallel Tracking and Mapping for Small AR Workspaces
G. Klein and D. W. Murray · 2007
Earlier work this paper cites.
KinectFusion: Real-Time 3D Reconstruction and Interaction Using a Moving Depth Camera
S. Izadi, D. Kim, O. Hilliges, D. Molyneaux, R. A. Newcombe, P. Kohli, J. Shotton, S. Hodges, D. Freeman, A. J. Davison, and A. Fitzgibbon · 2011
Earlier work this paper cites.
KinectFusion: Real-Time Dense Surface Mapping and Tracking
R. A. Newcombe, S. Izadi, O. Hilliges, D. Molyneaux, D. Kim, A. J. Davison, P. Kohli, J. Shotton, S. Hodges, and A. Fitzgibbon · 2011
Earlier work this paper cites.
Dense reconstruction using 3d object shape priors
A. Dame, V. A. Prisacariu, C. Y. Ren, and I. Reid · 2013
Earlier work this paper cites.
SLAM++: Simultaneous Localisation and Mapping at the Level of Objects
R. F. Salas-Moreno, R. A. Newcombe, H. Strasdat, P. H. J. Kelly, and A. J. Davison · 2013
Earlier work this paper cites.
Auto-Encoding Variational Bayes
D. P. Kingma and M. Welling · 2014
Earlier work this paper cites.
ShapeNet: An information-rich 3d model repository
A. X. Chang, T. Funkhouser, L. Guibas, P. Hanrahan, Q. Huang, Z. Li, S. Savarese, M. Savva, S. Song, H. Su, J. Xiao, L. Yi, and F. Yu · 2015
Earlier work this paper cites.
ImageNet Large Scale Visual Recognition Challenge
O. Russakovsky, J. Deng, H. Su, J. Krause, S. Satheesh, S. Ma, Z. Huang, A. Karpathy, A. Khosla, M. Bernstein, A. C. Berg, and L. Fei-Fei · 2015
Earlier work this paper cites.
Very Deep Convolutional Networks for Large-Scale Image Recognition
K. Simonyan and A. Zisserman · 2015
Earlier work this paper cites.
Learning structured output representation using deep conditional generative models
K. Sohn, H. Lee, and X. Yan · 2015
Earlier work this paper cites.
ElasticFusion: Dense SLAM without a pose graph
T. Whelan, S. Leutenegger, R. F. Salas-Moreno, B. Glocker, and A. J. Davison · 2015
Earlier work this paper cites.
3D ShapeNets: A Deep Representation for Volumetric Shapes
Z. Wu, S. Song, A. Khosla, F. Yu, L. Zhang, X. Tang, and J. Xiao · 2015
Earlier work this paper cites.
3d-r2n2: A unified approach for single and multi-view 3d object reconstruction
C. Choy, D. Xu, J. Gwak, K. Chen, and S. Savarese · 2016
Cited alongside, same era.
Learning a probabilistic latent space of object shapes via 3d generative-adversarial modeling
J. Wu, C. Zhang, T. Xue, W. Freeman, and J. Tenenbaum · 2016
Cited alongside, same era.
Direct sparse odometry
J. Engel, V. Koltun, and D. Cremers · 2017
Cited alongside, same era.
Samp: shape and motion priors for 4d vehicle reconstruction
F. Engelmann, J. Stückler, and B. Leibe · 2017
Cited alongside, same era.
Mask r-cnn
K. He, G. Gkioxari, P. Dollár, and R. Girshick · 2017
Cited alongside, same era.
SemanticFusion: Dense 3D semantic mapping with convolutional neural networks
J. McCormac, A. Handa, A. J. Davison, and S. Leutenegger · 2017
Cited alongside, same era.
Open3D: A modern library for 3D data processing
Q.-Y. Zhou, J. Park, and V. Koltun · 2018
Later among the works it cites.
Object-centric photometric bundle adjustment with deep shape prior
R. Zhu, C. Wang, C.-H. Lin, Z. Wang, and S. Lucey · 2018
Later among the works it cites.
Mesh r-cnn
G. Gkioxari, J. Malik, and J. Johnson · 2019
Later among the works it cites.
Deep-slam++: Object-level rgbd slam based on class-specific deep shape priors
L. Hu, W. Xu, K. Huang, and L. Kneip · 2019
Later among the works it cites.
Single-view object shape reconstruction using deep shape prior and silhouette
K. Li, R. Garg, M. Cai, and I. Reid · 2019
Later among the works it cites.
Occupancy networks: Learning 3d reconstruction in function space
alphaXiv searches the wider corpus for related work and actual follow-ups.
alphaXiv is searching for related work…
Meaningful maps with object-oriented semantic mapping
N. Sünderhauf, T. T. Pham, Y. Latif, M. Milford, and I. Reid · 2017
Cited alongside, same era.
Multi-view supervision for single-view reconstruction via differentiable ray consistency
S. Tulsiani, T. Zhou, A. A. Efros, and J. Malik · 2017
Cited alongside, same era.
Marrnet: 3d shape reconstruction via 2.5 d sketches
J. Wu, Y. Wang, T. Xue, X. Sun, B. Freeman, and J. Tenenbaum · 2017
Cited alongside, same era.
DA-RNN: Semantic mapping with data associated recurrent neural networks
Y. Xiang and D. Fox · 2017
Cited alongside, same era.
3d-rcnn: Instance-level 3d object reconstruction via render-and-compare
A. Kundu, Y. Li, and J. M. Rehg · 2018
Cited alongside, same era.
Pix3d: Dataset and methods for single-image 3d shape modeling
X. Sun, J. Wu, X. Zhang, Z. Zhang, C. Zhang, T. Xue, J. B. Tenenbaum, and W. T. Freeman · 2018
Cited alongside, same era.
L. Mescheder, M. Oechsle, M. Niemeyer, S. Nowozin, and A. Geiger · 2019
Later among the works it cites.
Deepsdf: Learning continuous signed distance functions for shape representation
J. J. Park, P. Florence, J. Straub, R. Newcombe, and S. Lovegrove · 2019
Later among the works it cites.
Superquadrics revisited: Learning 3d shape parsing beyond cuboids
D. Paschalidou, A. O. Ulusoy, and A. Geiger · 2019
Later among the works it cites.
Directshape: Photometric alignment of shape priors for visual vehicle pose and shape estimation
R. Wang, N. Yang, J. Stueckler, and D. Cremers · 2019
Later among the works it cites.
Sdfdiff: Differentiable rendering of signed distance fields for 3d shape optimization
Y. Jiang, D. Ji, Z. Han, and M. Zwicker · 2020
Closest in time.
Frodo: From detections to 3d objects
K. Li, M. Rünz, M. Tang, L. Ma, C. Kong, T. Schmidt, I. Reid, L. Agapito, J. Straub, S. Lovegrove, et al · 2020
Closest in time.
Dist: Rendering deep implicit signed distance function with differentiable sphere tracing
S. Liu, Y. Zhang, S. Peng, B. Shi, M. Pollefeys, and Z. Cui · 2020
Closest in time.
Differentiable volumetric rendering: Learning implicit 3d representations without 3d supervision
M. Niemeyer, L. Mescheder, M. Oechsle, and A. Geiger · 2020
Closest in time.