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Found 9 papers, 7 papers with code
Pixel-GS: Density Control with Pixel-aware Gradient for 3D Gaussian Splatting
3D Gaussian Splatting (3DGS) has demonstrated impressive novel view synthesis results while advancing real-time rendering performance.
AlignMiF: Geometry-Aligned Multimodal Implicit Field for LiDAR-Camera Joint Synthesis
Through extensive experiments across various datasets and scenes, we demonstrate the effectiveness of our approach in facilitating better interaction between LiDAR and camera modalities within a unified neural field.
Objects With Lighting: A Real-World Dataset for Evaluating Reconstruction and Rendering for Object Relighting
Reconstructing an object from photos and placing it virtually in a new environment goes beyond the standard novel view synthesis task as the appearance of the object has to not only adapt to the novel viewpoint but also to the new lighting conditions and yet evaluations of inverse rendering methods rely on novel view synthesis data or simplistic synthetic datasets for quantitative analysis.
CorresNeRF: Image Correspondence Priors for Neural Radiance Fields
We present CorresNeRF, a novel method that leverages image correspondence priors computed by off-the-shelf methods to supervise NeRF training.
LiDAR-NeRF: Novel LiDAR View Synthesis via Neural Radiance Fields
We address this challenge by formulating, to the best of our knowledge, the first differentiable end-to-end LiDAR rendering framework, LiDAR-NeRF, leveraging a neural radiance field (NeRF) to facilitate the joint learning of geometry and the attributes of 3D points.
Point Transformer V2: Grouped Vector Attention and Partition-based Pooling
In this work, we analyze the limitations of the Point Transformer and propose our powerful and efficient Point Transformer V2 model with novel designs that overcome the limitations of previous work.
Ranked #1 on
3D Semantic Segmentation
on nuScenes
ASH: A Modern Framework for Parallel Spatial Hashing in 3D Perception
Unlike existing GPU hash maps, the ASH framework provides a versatile tensor interface, hiding low-level details from the users.
nGraph-HE: A Graph Compiler for Deep Learning on Homomorphically Encrypted Data
Homomorphic encryption (HE)--the ability to perform computations on encrypted data--is an attractive remedy to increasing concerns about data privacy in the field of machine learning.
Cryptography and Security
Intel nGraph: An Intermediate Representation, Compiler, and Executor for Deep Learning
The current approach, which we call "direct optimization", requires deep changes within each framework to improve the training performance for each hardware backend (CPUs, GPUs, FPGAs, ASICs) and requires $\mathcal{O}(fp)$ effort; where $f$ is the number of frameworks and $p$ is the number of platforms.
