Search Results for author:
Found 14 papers, 10 papers with code
Can 3D Vision-Language Models Truly Understand Natural Language?
To test the language understandability of 3D-VL models, we first propose a language robustness task for systematically assessing 3D-VL models across various tasks, benchmarking their performance when presented with different language style variants.
V-IRL: Grounding Virtual Intelligence in Real Life
There is a sensory gulf between the Earth that humans inhabit and the digital realms in which modern AI agents are created.
Lowis3D: Language-Driven Open-World Instance-Level 3D Scene Understanding
To address this challenge, we propose to harness pre-trained vision-language (VL) foundation models that encode extensive knowledge from image-text pairs to generate captions for multi-view images of 3D scenes.
Ranked #3 on
3D Open-Vocabulary Instance Segmentation
on S3DIS
3D Open-Vocabulary Instance Segmentation
Instance Segmentation
+4
RegionPLC: Regional Point-Language Contrastive Learning for Open-World 3D Scene Understanding
Existing 3D scene understanding tasks have achieved high performance on close-set benchmarks but fail to handle novel categories in real-world applications.
PLA: Language-Driven Open-Vocabulary 3D Scene Understanding
Open-vocabulary scene understanding aims to localize and recognize unseen categories beyond the annotated label space.
Ranked #2 on
3D Open-Vocabulary Instance Segmentation
on S3DIS
3D Open-Vocabulary Instance Segmentation
Contrastive Learning
+4
Towards Efficient 3D Object Detection with Knowledge Distillation
Then, we build a benchmark to assess existing KD methods developed in the 2D domain for 3D object detection upon six well-constructed teacher-student pairs.
DODA: Data-oriented Sim-to-Real Domain Adaptation for 3D Semantic Segmentation
Deep learning approaches achieve prominent success in 3D semantic segmentation.
Knowledge Distillation as Efficient Pre-training: Faster Convergence, Higher Data-efficiency, and Better Transferability
Large-scale pre-training has been proven to be crucial for various computer vision tasks.
ST3D++: Denoised Self-training for Unsupervised Domain Adaptation on 3D Object Detection
These specific designs enable the detector to be trained on meticulously refined pseudo labeled target data with denoised training signals, and thus effectively facilitate adapting an object detector to a target domain without requiring annotations.
Re-distributing Biased Pseudo Labels for Semi-supervised Semantic Segmentation: A Baseline Investigation
In this paper, we present a simple and yet effective Distribution Alignment and Random Sampling (DARS) method to produce unbiased pseudo labels that match the true class distribution estimated from the labeled data.
ST3D: Self-training for Unsupervised Domain Adaptation on 3D Object Detection
Then, the detector is iteratively improved on the target domain by alternatively conducting two steps, which are the pseudo label updating with the developed quality-aware triplet memory bank and the model training with curriculum data augmentation.
PV-RCNN: The Top-Performing LiDAR-only Solutions for 3D Detection / 3D Tracking / Domain Adaptation of Waymo Open Dataset Challenges
In this technical report, we present the top-performing LiDAR-only solutions for 3D detection, 3D tracking and domain adaptation three tracks in Waymo Open Dataset Challenges 2020.
An Adversarial Perturbation Oriented Domain Adaptation Approach for Semantic Segmentation
In contrast to adversarial alignment, we propose to explicitly train a domain-invariant classifier by generating and defensing against pointwise feature space adversarial perturbations.
Larger Norm More Transferable: An Adaptive Feature Norm Approach for Unsupervised Domain Adaptation
Domain adaptation enables the learner to safely generalize into novel environments by mitigating domain shifts across distributions.
Ranked #7 on
Domain Adaptation
on ImageCLEF-DA
