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Found 37 papers, 19 papers with code
InTeX: Interactive Text-to-texture Synthesis via Unified Depth-aware Inpainting
Text-to-texture synthesis has become a new frontier in 3D content creation thanks to the recent advances in text-to-image models.
LGM: Large Multi-View Gaussian Model for High-Resolution 3D Content Creation
2) 3D Backbone: We present an asymmetric U-Net as a high-throughput backbone operating on multi-view images, which can be produced from text or single-view image input by leveraging multi-view diffusion models.
DreamGaussian4D: Generative 4D Gaussian Splatting
Remarkable progress has been made in 4D content generation recently.
HumanGaussian: Text-Driven 3D Human Generation with Gaussian Splatting
In this paper, we propose an efficient yet effective framework, HumanGaussian, that generates high-quality 3D humans with fine-grained geometry and realistic appearance.
DreamGaussian: Generative Gaussian Splatting for Efficient 3D Content Creation
In contrast to the occupancy pruning used in Neural Radiance Fields, we demonstrate that the progressive densification of 3D Gaussians converges significantly faster for 3D generative tasks.
Interactive Segment Anything NeRF with Feature Imitation
We propose to imitate the backbone feature of off-the-shelf perception models to achieve zero-shot semantic segmentation with NeRF.
VisionLLM: Large Language Model is also an Open-Ended Decoder for Vision-Centric Tasks
We hope this model can set a new baseline for generalist vision and language models.
Real-time 3D Semantic Scene Completion Via Feature Aggregation and Conditioned Prediction
In this paper, we propose a real-time semantic scene completion method with a feature aggregation strategy and conditioned prediction module.
Delicate Textured Mesh Recovery from NeRF via Adaptive Surface Refinement
Neural Radiance Fields (NeRF) have constituted a remarkable breakthrough in image-based 3D reconstruction.
Graph Signal Sampling for Inductive One-Bit Matrix Completion: a Closed-form Solution
Inductive one-bit matrix completion is motivated by modern applications such as recommender systems, where new users would appear at test stage with the ratings consisting of only ones and no zeros.
Real-time Neural Radiance Talking Portrait Synthesis via Audio-spatial Decomposition
While dynamic Neural Radiance Fields (NeRF) have shown success in high-fidelity 3D modeling of talking portraits, the slow training and inference speed severely obstruct their potential usage.
D$^3$ETR: Decoder Distillation for Detection Transformer
Specifically, Adaptive Matching applies bipartite matching to adaptively match the outputs of the teacher and the student in each decoder layer, while Fixed Matching fixes the correspondence between the outputs of the teacher and the student with the same object queries, with the teacher's fixed object queries fed to the decoder of the student as an auxiliary group.
JVLDLoc: a Joint Optimization of Visual-LiDAR Constraints and Direction Priors for Localization in Driving Scenario
The ability for a moving agent to localize itself in environment is the basic demand for emerging applications, such as autonomous driving, etc.
Group DETR: Fast DETR Training with Group-Wise One-to-Many Assignment
Detection transformer (DETR) relies on one-to-one assignment, assigning one ground-truth object to one prediction, for end-to-end detection without NMS post-processing.
Conditional DETR V2: Efficient Detection Transformer with Box Queries
Inspired by Conditional DETR, an improved DETR with fast training convergence, that presented box queries (originally called spatial queries) for internal decoder layers, we reformulate the object query into the format of the box query that is a composition of the embeddings of the reference point and the transformation of the box with respect to the reference point.
Compressible-composable NeRF via Rank-residual Decomposition
To circumvent the hurdle, in this paper, we present an explicit neural field representation that enables efficient and convenient manipulation of models.
Point Scene Understanding via Disentangled Instance Mesh Reconstruction
Semantic scene reconstruction from point cloud is an essential and challenging task for 3D scene understanding.
MaskGroup: Hierarchical Point Grouping and Masking for 3D Instance Segmentation
For instance, our approach achieves a 66. 4\% mAP with the 0. 5 IoU threshold on the ScanNetV2 test set, which is 1. 9\% higher than the state-of-the-art method.
Ranked #6 on
3D Instance Segmentation
on S3DIS
Context Autoencoder for Self-Supervised Representation Learning
The pretraining tasks include two tasks: masked representation prediction - predict the representations for the masked patches, and masked patch reconstruction - reconstruct the masked patches.
Not All Voxels Are Equal: Semantic Scene Completion from the Point-Voxel Perspective
Firstly, we transfer the voxelized scenes to point clouds by removing these visible empty voxels and adopt a deep point stream to capture semantic information from the scene efficiently.
Ranked #4 on
3D Semantic Scene Completion
on NYUv2
Conditional DETR for Fast Training Convergence
Our approach, named conditional DETR, learns a conditional spatial query from the decoder embedding for decoder multi-head cross-attention.
Joint Implicit Image Function for Guided Depth Super-Resolution
Inspired by the recent progress in implicit neural representation, we propose to formulate the guided super-resolution as a neural implicit image interpolation problem, where we take the form of a general image interpolation but use a novel Joint Implicit Image Function (JIIF) representation to learn both the interpolation weights and values.
Semi-Supervised Semantic Segmentation with Cross Pseudo Supervision
Our approach imposes the consistency on two segmentation networks perturbed with different initialization for the same input image.
Ranked #2 on
Semi-Supervised Semantic Segmentation
on WoodScape
Semantic Point Completion Network for 3D Semantic Scene Completion
In this work, a Semantic Point Completion Network (SPCNet) is proposed to address SSC in the point cloud space.
Ranked #8 on
3D Semantic Scene Completion
on NYUv2
Malleable 2.5D Convolution: Learning Receptive Fields along the Depth-axis for RGB-D Scene Parsing
In this paper, we propose a novel operator called malleable 2. 5D convolution to learn the receptive field along the depth-axis.
Ranked #46 on
Semantic Segmentation
on NYU Depth v2
Bi-directional Cross-Modality Feature Propagation with Separation-and-Aggregation Gate for RGB-D Semantic Segmentation
Depth information has proven to be a useful cue in the semantic segmentation of RGB-D images for providing a geometric counterpart to the RGB representation.
3D Sketch-aware Semantic Scene Completion via Semi-supervised Structure Prior
To this end, we first propose a novel 3D sketch-aware feature embedding to explicitly encode geometric information effectively and efficiently.
3D Semantic Scene Completion from a single RGB image
Hallucination
Neural Style Transfer via Meta Networks
Recent works on style transfer typically need to train image transformation networks for every new style, and the style is encoded in the network parameters by enormous iterations of stochastic gradient descent, which lacks the generalization ability to new style in the inference stage.
Meta Networks for Neural Style Transfer
Recent works on style transfer typically need to train image transformation networks for every new style, and the style is encoded in the network parameters by enormous iterations of stochastic gradient descent.
Semantic Segmentation via Structured Patch Prediction, Context CRF and Guidance CRF
The proposed joint model also employs a guidance CRF to further enhance the segmentation performance.
Weighted Residuals for Very Deep Networks
The weighted residual network is able to learn to combine residuals from different layers effectively and efficiently.
Fast Semantic Image Segmentation with High Order Context and Guided Filtering
This paper describes a fast and accurate semantic image segmentation approach that encodes not only the discriminative features from deep neural networks, but also the high-order context compatibility among adjacent objects as well as low level image features.
Similarity-Aware Patchwork Assembly for Depth Image Super-Resolution
This paper describes a patchwork assembly algorithm for depth image super-resolution.
Fast Approximate $K$-Means via Cluster Closures
Traditional $k$-means is an iterative algorithm---in each iteration new cluster centers are computed and each data point is re-assigned to its nearest center.
Fast Neighborhood Graph Search using Cartesian Concatenation
This structure augments the neighborhood graph with a bridge graph.
Scalable $k$-NN graph construction
The $k$-NN graph has played a central role in increasingly popular data-driven techniques for various learning and vision tasks; yet, finding an efficient and effective way to construct $k$-NN graphs remains a challenge, especially for large-scale high-dimensional data.
Supervised Kernel Descriptors for Visual Recognition
In visual recognition tasks, the design of low level image feature representation is fundamental.
