Andreas Geiger

Publications of Songyou Peng

UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction (oral)
M. Oechsle, S. Peng and A. Geiger
International Conference on Computer Vision (ICCV), 2021
Abstract: Neural implicit 3D representations have emerged as a powerful paradigm for reconstructing surfaces from multi-view images and synthesizing novel views. Unfortunately, existing methods such as DVR or IDR require accurate per-pixel object masks as supervision. At the same time, neural radiance fields have revolutionized novel view synthesis. However, NeRF's estimated volume density does not admit accurate surface reconstruction. Our key insight is that implicit surface models and radiance fields can be formulated in a unified way, enabling both surface and volume rendering using the same model. This unified perspective enables novel, more efficient sampling procedures and the ability to reconstruct accurate surfaces without input masks. We compare our method on the DTU, BlendedMVS, and a synthetic indoor dataset. Our experiments demonstrate that we outperform NeRF in terms of reconstruction quality while performing on par with IDR without requiring masks.
Latex Bibtex Citation:
@INPROCEEDINGS{Oechsle2021ICCV,
  author = {Michael Oechsle and Songyou Peng and Andreas Geiger},
  title = {UNISURF: Unifying Neural Implicit Surfaces and Radiance Fields for Multi-View Reconstruction},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year = {2021}
}
KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs
C. Reiser, S. Peng, Y. Liao and A. Geiger
International Conference on Computer Vision (ICCV), 2021
Abstract: NeRF synthesizes novel views of a scene with unprecedented quality by fitting a neural radiance field to RGB images. However, NeRF requires querying a deep Multi-Layer Perceptron (MLP) millions of times, leading to slow rendering times, even on modern GPUs. In this paper, we demonstrate that significant speed-ups are possible by utilizing thousands of tiny MLPs instead of one single large MLP. In our setting, each individual MLP only needs to represent parts of the scene, thus smaller and faster-to-evaluate MLPs can be used. By combining this divide-and-conquer strategy with further optimizations, rendering is accelerated by two orders of magnitude compared to the original NeRF model without incurring high storage costs. Further, using teacher-student distillation for training, we show that this speed-up can be achieved without sacrificing visual quality..
Latex Bibtex Citation:
@INPROCEEDINGS{Reiser2021ICCV,
  author = {Christian Reiser and Songyou Peng and Yiyi Liao and Andreas Geiger},
  title = {KiloNeRF: Speeding up Neural Radiance Fields with Thousands of Tiny MLPs},
  booktitle = {International Conference on Computer Vision (ICCV)},
  year = {2021}
}
Convolutional Occupancy Networks (spotlight)
S. Peng, M. Niemeyer, L. Mescheder, M. Pollefeys and A. Geiger
European Conference on Computer Vision (ECCV), 2020
Abstract: Recently, implicit neural representations have gained popularity for learning-based 3D reconstruction. While demonstrating promising results, most implicit approaches are limited to comparably simple geometry of single objects and do not scale to more complicated or large-scale scenes. The key limiting factor of implicit methods is their simple fully-connected network architecture which does not allow for integrating local information in the observations or incorporating inductive biases such as translational equivariance. In this paper, we propose Convolutional Occupancy Networks, a more flexible implicit representation for detailed reconstruction of objects and 3D scenes. By combining convolutional encoders with implicit occupancy decoders, our model incorporates inductive biases, enabling structured reasoning in 3D space. We investigate the effectiveness of the proposed representation by reconstructing complex geometry from noisy point clouds and low-resolution voxel representations. We empirically find that our method enables the fine-grained implicit 3D reconstruction of single objects, scales to large indoor scenes, and generalizes well from synthetic to real data.
Latex Bibtex Citation:
@INPROCEEDINGS{Peng2020ECCV,
  author = {Songyou Peng and Michael Niemeyer and Lars Mescheder and Marc Pollefeys and Andreas Geiger},
  title = {Convolutional Occupancy Networks},
  booktitle = {European Conference on Computer Vision (ECCV)},
  year = {2020}
}


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