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Found 11 papers, 6 papers with code
Prompting Depth Anything for 4K Resolution Accurate Metric Depth Estimation
Prompts play a critical role in unleashing the power of language and vision foundation models for specific tasks.
StreetCrafter: Street View Synthesis with Controllable Video Diffusion Models
This paper aims to tackle the problem of photorealistic view synthesis from vehicle sensor data.
Street Gaussians: Modeling Dynamic Urban Scenes with Gaussian Splatting
Recent methods extend NeRF by incorporating tracked vehicle poses to animate vehicles, enabling photo-realistic view synthesis of dynamic urban street scenes.
EasyVolcap: Accelerating Neural Volumetric Video Research
Volumetric video is a technology that digitally records dynamic events such as artistic performances, sporting events, and remote conversations.
4K4D: Real-Time 4D View Synthesis at 4K Resolution
Experiments show that our representation can be rendered at over 400 FPS on the DNA-Rendering dataset at 1080p resolution and 80 FPS on the ENeRF-Outdoor dataset at 4K resolution using an RTX 4090 GPU, which is 30x faster than previous methods and achieves the state-of-the-art rendering quality.
Im4D: High-Fidelity and Real-Time Novel View Synthesis for Dynamic Scenes
This paper aims to tackle the challenge of dynamic view synthesis from multi-view videos.
Neural Scene Chronology
Specifically, we represent the scene as a space-time radiance field with a per-image illumination embedding, where temporally-varying scene changes are encoded using a set of learned step functions.
AutoRecon: Automated 3D Object Discovery and Reconstruction
A fully automated object reconstruction pipeline is crucial for digital content creation.
Painting 3D Nature in 2D: View Synthesis of Natural Scenes from a Single Semantic Mask
We introduce a novel approach that takes a single semantic mask as input to synthesize multi-view consistent color images of natural scenes, trained with a collection of single images from the Internet.
Neural 3D Scene Reconstruction with the Manhattan-world Assumption
Based on the Manhattan-world assumption, planar constraints are employed to regularize the geometry in floor and wall regions predicted by a 2D semantic segmentation network.
Efficient Neural Radiance Fields for Interactive Free-viewpoint Video
We propose a novel scene representation, called ENeRF, for the fast creation of interactive free-viewpoint videos.
