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Found 26 papers, 12 papers with code
TANet: Towards Fully Automatic Tooth Arrangement
Determining optimal target tooth arrangements is a key step of treatment planning in digital orthodontics.
IS-Fusion: Instance-Scene Collaborative Fusion for Multimodal 3D Object Detection
HSF applies Point-to-Grid and Grid-to-Region transformers to capture the multimodal scene context at different granularities.
HUNTER: Unsupervised Human-centric 3D Detection via Transferring Knowledge from Synthetic Instances to Real Scenes
Human-centric 3D scene understanding has recently drawn increasing attention, driven by its critical impact on robotics.
Learning to Adapt SAM for Segmenting Cross-domain Point Clouds
Unsupervised domain adaptation (UDA) in 3D segmentation tasks presents a formidable challenge, primarily stemming from the sparse and unordered nature of point cloud data.
Model2Scene: Learning 3D Scene Representation via Contrastive Language-CAD Models Pre-training
In this paper, we propose Model2Scene, a novel paradigm that learns free 3D scene representation from Computer-Aided Design (CAD) models and languages.
UniSeg: A Unified Multi-Modal LiDAR Segmentation Network and the OpenPCSeg Codebase
Besides, we construct the OpenPCSeg codebase, which is the largest and most comprehensive outdoor LiDAR segmentation codebase.
Ranked #2 on
3D Semantic Segmentation
on SemanticKITTI
(using extra training data)
Human-centric Scene Understanding for 3D Large-scale Scenarios
Human-centric scene understanding is significant for real-world applications, but it is extremely challenging due to the existence of diverse human poses and actions, complex human-environment interactions, severe occlusions in crowds, etc.
See More and Know More: Zero-shot Point Cloud Segmentation via Multi-modal Visual Data
They typically align visual features with semantic features obtained from word embedding by the supervision of seen classes' annotations.
Segment Any Point Cloud Sequences by Distilling Vision Foundation Models
Recent advancements in vision foundation models (VFMs) have opened up new possibilities for versatile and efficient visual perception.
Towards Label-free Scene Understanding by Vision Foundation Models
For nuImages and nuScenes datasets, the performance is 22. 1\% and 26. 8\% with improvements of 3. 5\% and 6. 0\%, respectively.
Robo3D: Towards Robust and Reliable 3D Perception against Corruptions
The robustness of 3D perception systems under natural corruptions from environments and sensors is pivotal for safety-critical applications.
Depth-NeuS: Neural Implicit Surfaces Learning for Multi-view Reconstruction Based on Depth Information Optimization
To address this problem, we propose a neural implicit surface learning method called Depth-NeuS based on depth information optimization for multi-view reconstruction.
Rethinking Range View Representation for LiDAR Segmentation
We show that, for the first time, a range view method is able to surpass the point, voxel, and multi-view fusion counterparts in the competing LiDAR semantic and panoptic segmentation benchmarks, i. e., SemanticKITTI, nuScenes, and ScribbleKITTI.
Ranked #4 on
3D Semantic Segmentation
on SemanticKITTI
CLIP2Scene: Towards Label-efficient 3D Scene Understanding by CLIP
For the first time, our pre-trained network achieves annotation-free 3D semantic segmentation with 20. 8% and 25. 08% mIoU on nuScenes and ScanNet, respectively.
Zero-shot point cloud segmentation by transferring geometric primitives
To this end, we propose a novel framework to learn the geometric primitives shared in seen and unseen categories' objects and employ a fine-grained alignment between language and the learned geometric primitives.
Towards 3D Scene Understanding by Referring Synthetic Models
Promising performance has been achieved for visual perception on the point cloud.
Referring Self-supervised Learning on 3D Point Cloud
In this paper, we study a new problem named Referring Self-supervised Learning (RSL) on 3D scene understanding: Given the 3D synthetic models with labels and the unlabeled 3D real scene scans, our goal is to distinguish the identical semantic objects on an unseen scene according to the referring synthetic 3D models.
PR-Net: Preference Reasoning for Personalized Video Highlight Detection
Personalized video highlight detection aims to shorten a long video to interesting moments according to a user's preference, which has recently raised the community's attention.
Distributed Attention for Grounded Image Captioning
We study the problem of weakly supervised grounded image captioning.
Structure-Aware Long Short-Term Memory Network for 3D Cephalometric Landmark Detection
Detecting 3D landmarks on cone-beam computed tomography (CBCT) is crucial to assessing and quantifying the anatomical abnormalities in 3D cephalometric analysis.
Semi-supervised Anatomical Landmark Detection via Shape-regulated Self-training
The global shape constraint is the inherent property of anatomical landmarks that provides valuable guidance for more consistent pseudo labelling of the unlabeled data, which is ignored in the previously semi-supervised methods.
Category Disentangled Context: Turning Category-irrelevant Features Into Treasures
Deep neural networks have achieved great success in computer vision, thanks to their ability in extracting category-relevant semantic features.
Unsupervised Learning of Intrinsic Structural Representation Points
The 3D structure points produced by our method encode the shape structure intrinsically and exhibit semantic consistency across all the shape instances with similar structures.
Cephalometric Landmark Detection by Attentive Feature Pyramid Fusion and Regression-Voting
Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.
Cephalometric Landmark Detection by AttentiveFeature Pyramid Fusion and Regression-Voting
Marking anatomical landmarks in cephalometric radiography is a critical operation in cephalometric analysis.
Attending Category Disentangled Global Context for Image Classification
In this paper, we propose a general framework for image classification using the attention mechanism and global context, which could incorporate with various network architectures to improve their performance.
