Search Results for author:
Found 9 papers, 7 papers with code
No Time to Train: Empowering Non-Parametric Networks for Few-shot 3D Scene Segmentation
To reduce the reliance on large-scale datasets, recent works in 3D segmentation resort to few-shot learning.
Point-Bind & Point-LLM: Aligning Point Cloud with Multi-modality for 3D Understanding, Generation, and Instruction Following
We introduce Point-Bind, a 3D multi-modality model aligning point clouds with 2D image, language, audio, and video.
Ranked #5 on
3D Question Answering (3D-QA)
on 3D MM-Vet
Less is More: Towards Efficient Few-shot 3D Semantic Segmentation via Training-free Networks
However, the prior pre-training stage not only introduces excessive time overhead, but also incurs a significant domain gap on `unseen' classes.
3D Semantic Segmentation
Few-shot 3D semantic segmentation
+1
Efficient Multi-View Inverse Rendering Using a Hybrid Differentiable Rendering Method
In this paper, we introduce a novel hybrid differentiable rendering method to efficiently reconstruct the 3D geometry and reflectance of a scene from multi-view images captured by conventional hand-held cameras.
Not All Features Matter: Enhancing Few-shot CLIP with Adaptive Prior Refinement
The popularity of Contrastive Language-Image Pre-training (CLIP) has propelled its application to diverse downstream vision tasks.
PointCLIP V2: Prompting CLIP and GPT for Powerful 3D Open-world Learning
In this paper, we first collaborate CLIP and GPT to be a unified 3D open-world learner, named as PointCLIP V2, which fully unleashes their potential for zero-shot 3D classification, segmentation, and detection.
Ranked #2 on
3D Open-Vocabulary Instance Segmentation
on STPLS3D
Conditional Generative Models for Simulation of EMG During Naturalistic Movements
As a solution to this problem, we propose a transfer learning approach, in which a conditional generative model is trained to mimic the output of an advanced numerical model.
Steerable Pyramid Transform Enables Robust Left Ventricle Quantification
First, the basis functions of SPT match the anatomical structure of the LV as well as the geometric characteristics of the estimated indices.
Learning to Navigate from Simulation via Spatial and Semantic Information Synthesis with Noise Model Embedding
While training an end-to-end navigation network in the real world is usually of high cost, simulation provides a safe and cheap environment in this training stage.
