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Found 12 papers, 9 papers with code
StableDreamer: Taming Noisy Score Distillation Sampling for Text-to-3D
Our analysis identifies the core of these challenges as the interaction among noise levels in the 2D diffusion process, the architecture of the diffusion network, and the 3D model representation.
Pseudo-Generalized Dynamic View Synthesis from a Video
In contrast, for dynamic scenes, scene-specific optimization techniques exist, but, to our best knowledge, there is currently no generalized method for dynamic novel view synthesis from a given monocular video.
FineRecon: Depth-aware Feed-forward Network for Detailed 3D Reconstruction
Recent works on 3D reconstruction from posed images have demonstrated that direct inference of scene-level 3D geometry without test-time optimization is feasible using deep neural networks, showing remarkable promise and high efficiency.
HyperDiffusion: Generating Implicit Neural Fields with Weight-Space Diffusion
HyperDiffusion operates directly on MLP weights and generates new neural implicit fields encoded by synthesized MLP parameters.
Generative Multiplane Images: Making a 2D GAN 3D-Aware
What is really needed to make an existing 2D GAN 3D-aware?
Texturify: Generating Textures on 3D Shape Surfaces
Texture cues on 3D objects are key to compelling visual representations, with the possibility to create high visual fidelity with inherent spatial consistency across different views.
RetrievalFuse: Neural 3D Scene Reconstruction with a Database
3D reconstruction of large scenes is a challenging problem due to the high-complexity nature of the solution space, in particular for generative neural networks.
FastDepth: Fast Monocular Depth Estimation on Embedded Systems
In this paper, we address the problem of fast depth estimation on embedded systems.
Invertibility of Convolutional Generative Networks from Partial Measurements
In this work, we present new theoretical results on convolutional generative neural networks, in particular their invertibility (i. e., the recovery of input latent code given the network output).
Self-supervised Sparse-to-Dense: Self-supervised Depth Completion from LiDAR and Monocular Camera
Depth completion, the technique of estimating a dense depth image from sparse depth measurements, has a variety of applications in robotics and autonomous driving.
Ranked #6 on
Depth Completion
on VOID
Sparse-to-Dense: Depth Prediction from Sparse Depth Samples and a Single Image
We consider the problem of dense depth prediction from a sparse set of depth measurements and a single RGB image.
Sparse Depth Sensing for Resource-Constrained Robots
We address the following question: is it possible to reconstruct the geometry of an unknown environment using sparse and incomplete depth measurements?
