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Found 27 papers, 17 papers with code
3D Focusing-and-Matching Network for Multi-Instance Point Cloud Registration
Multi-instance point cloud registration aims to estimate the pose of all instances of a model point cloud in the whole scene.
Deep Height Decoupling for Precise Vision-based 3D Occupancy Prediction
MGHS projects the 2D image features into multiple subspaces, where each grid contains features within reasonable height ranges.
3D Geometry-aware Deformable Gaussian Splatting for Dynamic View Synthesis
In this way, our solution achieves 3D geometry-aware deformation modeling, which enables improved dynamic view synthesis and 3D dynamic reconstruction.
Multi-Attribute Interactions Matter for 3D Visual Grounding
3D visual grounding aims to localize 3D objects described by free-form language sentences.
Improving Depth Completion via Depth Feature Upsampling
In this paper we visualize the internal feature maps to analyze how the network densifies the input sparse depth.
SPGroup3D: Superpoint Grouping Network for Indoor 3D Object Detection
To this end, we propose a novel superpoint grouping network for indoor anchor-free one-stage 3D object detection.
Ranked #5 on
3D Object Detection
on S3DIS
RigNet++: Semantic Assisted Repetitive Image Guided Network for Depth Completion
To tackle these challenges, we explore a repetitive design in our image guided network to gradually and sufficiently recover depth values.
Self-Supervised 3D Scene Flow Estimation Guided by Superpoints
In our superpoint generation module, we utilize the bidirectional flow information at the previous iteration to obtain the matching points of points and superpoint centers for soft point-to-superpoint association construction, in which the superpoints are generated for pairwise point clouds.
Efficient LiDAR Point Cloud Oversegmentation Network
Then, to generate homogeneous superpoints from the sparse LiDAR point cloud, we propose a LiDAR point grouping algorithm that simultaneously considers the similarity of point embeddings and the Euclidean distance of points in 3D space.
Learning Inter-Superpoint Affinity for Weakly Supervised 3D Instance Segmentation
Due to the few annotated labels of 3D point clouds, how to learn discriminative features of point clouds to segment object instances is a challenging problem.
Point Cloud Registration-Driven Robust Feature Matching for 3D Siamese Object Tracking
The core of Siamese feature matching is how to assign high feature similarity on the corresponding points between the template and search area for precise object localization.
Unsupervised Domain Adaptation for Point Cloud Semantic Segmentation via Graph Matching
Unsupervised domain adaptation for point cloud semantic segmentation has attracted great attention due to its effectiveness in learning with unlabeled data.
RA-Depth: Resolution Adaptive Self-Supervised Monocular Depth Estimation
In this paper, we propose a resolution adaptive self-supervised monocular depth estimation method (RA-Depth) by learning the scale invariance of the scene depth.
Generative Subgraph Contrast for Self-Supervised Graph Representation Learning
To this end, in this paper, we propose a novel adaptive subgraph generation based contrastive learning framework for efficient and robust self-supervised graph representation learning, and the optimal transport distance is utilized as the similarity metric between the subgraphs.
3D Siamese Transformer Network for Single Object Tracking on Point Clouds
Siamese network based trackers formulate 3D single object tracking as cross-correlation learning between point features of a template and a search area.
Domain Disentangled Generative Adversarial Network for Zero-Shot Sketch-Based 3D Shape Retrieval
Then, we develop a generative adversarial network that combines the domain-specific features of the seen categories with the aligned domain-invariant features to synthesize samples, where the synthesized samples of the unseen categories are generated by using the corresponding word embeddings.
Reliable Inlier Evaluation for Unsupervised Point Cloud Registration
In this paper, we propose a neighborhood consensus based reliable inlier evaluation method for robust unsupervised point cloud registration.
3D Siamese Voxel-to-BEV Tracker for Sparse Point Clouds
The Siamese shape-aware feature learning network can capture 3D shape information of the object to learn the discriminative features of the object so that the potential target from the background in sparse point clouds can be identified.
SSPU-Net: Self-Supervised Point Cloud Upsampling via Differentiable Rendering
To achieve this, we exploit the consistency between the input sparse point cloud and generated dense point cloud for the shapes and rendered images.
SSPC-Net: Semi-supervised Semantic 3D Point Cloud Segmentation Network
In order to reduce the number of annotated labels, we propose a semi-supervised semantic point cloud segmentation network, named SSPC-Net, where we train the semantic segmentation network by inferring the labels of unlabeled points from the few annotated 3D points.
Efficient 3D Point Cloud Feature Learning for Large-Scale Place Recognition
In this paper, we develop an efficient point cloud learning network (EPC-Net) to form a global descriptor for visual place recognition, which can obtain good performance and reduce computation memory and inference time.
Pyramid Point Cloud Transformer for Large-Scale Place Recognition
In order to obtain discriminative global descriptors, we construct a pyramid VLAD module to aggregate the multi-scale feature maps of point clouds into the global descriptors.
Ranked #3 on
3D Place Recognition
on Oxford RobotCar Dataset
Superpoint Network for Point Cloud Oversegmentation
Specifically, in our clustering network, we first jointly learn a soft point-superpoint association map from the coordinate and feature spaces of point clouds, where each point is assigned to the superpoint with a learned weight.
Cascaded Non-local Neural Network for Point Cloud Semantic Segmentation
In this paper, we propose a cascaded non-local neural network for point cloud segmentation.
Progressive Point Cloud Deconvolution Generation Network
Starting from the low-resolution point clouds, with the bilateral interpolation and max-pooling operations, the deconvolution network can progressively output high-resolution local and global feature maps.
Stacked Conditional Generative Adversarial Networks for Jointly Learning Shadow Detection and Shadow Removal
Specifically, a shadow image is fed into the first generator which produces a shadow detection mask.
Ranked #3 on
Shadow Removal
on SRD
Unsupervised Multi-Domain Image Translation with Domain-Specific Encoders/Decoders
Unsupervised Image-to-Image Translation achieves spectacularly advanced developments nowadays.
