9 citations · 22 across the 9 of their papers we have counts for
12 papers · 1 filter
ORBIT++: Benchmarking SfM in the Wild with 360° Video
Sara Sabour, Linyi Jin, Richard Tucker +9
Structure-from-Motion (SfM) is a cornerstone of 3D perception, yet current methods often fail when applied to complex videos involving challenging camera motions or dynamic scenes.…
Meshtryoshka: Differentiable Rendering of Real-World Scenes via Mesh Rasterization
David Charatan, Daniel Xu, Richard Szeliski +2
Differentiable rendering has emerged as a powerful approach for 3D reconstruction and novel view synthesis. State-of-the-art differentiable rendering methods combine a variety of c…
Binary Opacity Grids: Capturing Fine Geometric Detail for Mesh-Based View Synthesis
Christian Reiser, Stephan Garbin, Pratul P. Srinivasan +6
While surface-based view synthesis algorithms are appealing due to their low computational requirements, they often struggle to reproduce thin structures. In contrast, more expensi…
UniSDF: Unifying Neural Representations for High-Fidelity 3D Reconstruction of Complex Scenes with Reflections
Fangjinhua Wang, Marie-Julie Rakotosaona, Michael Niemeyer +3
Neural 3D scene representations have shown great potential for 3D reconstruction from 2D images. However, reconstructing real-world captures of complex scenes still remains a chall…
SMERF: Streamable Memory Efficient Radiance Fields for Real-Time Large-Scene Exploration
Daniel Duckworth, Peter Hedman, Christian Reiser +5
Recent techniques for real-time view synthesis have rapidly advanced in fidelity and speed, and modern methods are capable of rendering near-photorealistic scenes at interactive fr…
MERF: Memory-Efficient Radiance Fields for Real-time View Synthesis in Unbounded Scenes
Christian Reiser, Richard Szeliski, Dor Verbin +5
Neural radiance fields enable state-of-the-art photorealistic view synthesis. However, existing radiance field representations are either too compute-intensive for real-time render…
BakedSDF: Meshing Neural SDFs for Real-Time View Synthesis
Lior Yariv, Peter Hedman, Christian Reiser +5
We present a method for reconstructing high-quality meshes of large unbounded real-world scenes suitable for photorealistic novel view synthesis. We first optimize a hybrid neural…
Accidental Light Probes
Hong-Xing Yu, Samir Agarwala, Charles Herrmann +4
Recovering lighting in a scene from a single image is a fundamental problem in computer vision. While a mirror ball light probe can capture omnidirectional lighting, light probes a…
Animating Pictures with Eulerian Motion Fields
Aleksander Holynski, Brian Curless, Steven M. Seitz +1
In this paper, we demonstrate a fully automatic method for converting a still image into a realistic animated looping video. We target scenes with continuous fluid motion, such as…
Reducing Drift in Structure From Motion Using Extended Features
Aleksander Holynski, David Geraghty, Jan-Michael Frahm +2
Low-frequency long-range errors (drift) are an endemic problem in 3D structure from motion, and can often hamper reasonable reconstructions of the scene. In this paper, we present…
Consistent Video Depth Estimation
Xuan Luo, Jia-Bin Huang, Richard Szeliski +2
We present an algorithm for reconstructing dense, geometrically consistent depth for all pixels in a monocular video. We leverage a conventional structure-from-motion reconstructio…
SynSin: End-to-end View Synthesis from a Single Image
Olivia Wiles, Georgia Gkioxari, Richard Szeliski +1
Single image view synthesis allows for the generation of new views of a scene given a single input image. This is challenging, as it requires comprehensively understanding the 3D s…