Search Results for author:
Found 19 papers, 16 papers with code
SinMPI: Novel View Synthesis from a Single Image with Expanded Multiplane Images
This paper proposes SinMPI, a novel method that uses an expanded multiplane image (MPI) as the 3D scene representation to significantly expand the perspective range of MPI and generate high-quality novel views from a large multiplane space.
Point Transformer V3: Simpler, Faster, Stronger
This paper is not motivated to seek innovation within the attention mechanism.
Ranked #1 on
Semantic Segmentation
on S3DIS
(using extra training data)
Learning the Geodesic Embedding with Graph Neural Networks
Then, we can compute the geodesic distance between a pair of points using our decoding function, which requires only several matrix multiplications and can be massively parallelized on GPUs.
Locally Attentional SDF Diffusion for Controllable 3D Shape Generation
Our method is built on a two-stage diffusion model.
OctFormer: Octree-based Transformers for 3D Point Clouds
To combat this issue, several works divide point clouds into non-overlapping windows and constrain attentions in each local window.
Ranked #4 on
3D Semantic Segmentation
on ScanNet200
3D Feature Prediction for Masked-AutoEncoder-Based Point Cloud Pretraining
Masked autoencoders (MAE) have recently been introduced to 3D self-supervised pretraining for point clouds due to their great success in NLP and computer vision.
Swin3D: A Pretrained Transformer Backbone for 3D Indoor Scene Understanding
We pretrained a large {\SST} model on a synthetic Structured3D dataset, which is an order of magnitude larger than the ScanNet dataset.
Ranked #2 on
3D Object Detection
on S3DIS
(using extra training data)
Randomized Quantization: A Generic Augmentation for Data Agnostic Self-supervised Learning
Self-supervised representation learning follows a paradigm of withholding some part of the data and tasking the network to predict it from the remaining part.
SDF-StyleGAN: Implicit SDF-Based StyleGAN for 3D Shape Generation
We further complement the evaluation metrics of 3D generative models with the shading-image-based Fr\'echet inception distance (FID) scores to better assess visual quality and shape distribution of the generated shapes.
Dual Octree Graph Networks for Learning Adaptive Volumetric Shape Representations
Our method encodes the volumetric field of a 3D shape with an adaptive feature volume organized by an octree and applies a compact multilayer perceptron network for mapping the features to the field value at each 3D position.
Semi-supervised 3D shape segmentation with multilevel consistency and part substitution
We propose an effective semi-supervised method for learning 3D segmentations from a few labeled 3D shapes and a large amount of unlabeled 3D data.
Interpolation-Aware Padding for 3D Sparse Convolutional Neural Networks
For fine-grained 3D vision tasks where point-wise features are essential, like semantic segmentation and 3D detection, our network achieves higher prediction accuracy than the existing networks using the nearest neighbor interpolation or the normalized trilinear interpolation with the zero-padding or the octree-padding scheme.
Spline Positional Encoding for Learning 3D Implicit Signed Distance Fields
Multilayer perceptrons (MLPs) have been successfully used to represent 3D shapes implicitly and compactly, by mapping 3D coordinates to the corresponding signed distance values or occupancy values.
Deep Implicit Moving Least-Squares Functions for 3D Reconstruction
We incorporate IMLS surface generation into deep neural networks for inheriting both the flexibility of point sets and the high quality of implicit surfaces.
Unsupervised 3D Learning for Shape Analysis via Multiresolution Instance Discrimination
Although unsupervised feature learning has demonstrated its advantages to reducing the workload of data labeling and network design in many fields, existing unsupervised 3D learning methods still cannot offer a generic network for various shape analysis tasks with competitive performance to supervised methods.
Ranked #2 on
3D Semantic Segmentation
on PartNet
3D Point Cloud Linear Classification
3D Semantic Segmentation
Deep Octree-based CNNs with Output-Guided Skip Connections for 3D Shape and Scene Completion
Acquiring complete and clean 3D shape and scene data is challenging due to geometric occlusion and insufficient views during 3D capturing.
Adaptive O-CNN: A Patch-based Deep Representation of 3D Shapes
The Adaptive O-CNN encoder takes the planar patch normal and displacement as input and performs 3D convolutions only at the octants at each level, while the Adaptive O-CNN decoder infers the shape occupancy and subdivision status of octants at each level and estimates the best plane normal and displacement for each leaf octant.
O-CNN: Octree-based Convolutional Neural Networks for 3D Shape Analysis
We present O-CNN, an Octree-based Convolutional Neural Network (CNN) for 3D shape analysis.
Ranked #4 on
3D Object Classification
on ModelNet40
Mesh Denoising via Cascaded Normal Regression
At runtime, our method applies the learned cascaded regression functions to a noisy input mesh and reconstructs the denoised mesh from the output facet normals.
