Progress in 3D computer vision tasks demands a huge amount of data, yet
annotating multi-view images with 3D-consistent annotations, or point clouds
with part segmentation is both time-consuming and challenging. This paper
introduces DatasetNeRF, a novel approach capable of generating infinite,
high-quality 3D-consistent 2D annotations alongside 3D point cloud
segmentations, while utilizing minimal 2D human-labeled annotations.
Specifically, we leverage the strong semantic prior within a 3D generative
model to train a semantic decoder, requiring only a handful of fine-grained
labeled samples. Once trained, the decoder efficiently generalizes across the
latent space, enabling the generation of infinite data. The generated data is
applicable across various computer vision tasks, including video segmentation
and 3D point cloud segmentation. Our approach not only surpasses baseline
models in segmentation quality, achieving superior 3D consistency and
segmentation precision on individual images, but also demonstrates versatility
by being applicable to both articulated and non-articulated generative models.
Furthermore, we explore applications stemming from our approach, such as
3D-aware semantic editing and 3D inversion.

该论文介绍了 DatasetNeRF，一种新颖的方法，它能够生成无限数量、高质量的与 3D 一致的 2D 注释和 3D 点云分割，同时利用最少的 2D 人工标注。

DatasetNeRF：基于生成辐射场的高效三维感知数据工厂

DatasetNeRF: Efficient 3D-aware Data Factory with Generative Radiance  Fields

This paper presents a deep learning architecture for the semantic decoder
component of a Statistical Spoken Dialogue System. In a slot-filling dialogue,
the semantic decoder predicts the dialogue act and a set of slot-value pairs
from a set of n-best hypotheses returned by the Automatic Speech Recognition.
Most current models for spoken language understanding assume (i) word-aligned
semantic annotations as in sequence taggers and (ii) delexicalisation, or a
mapping of input words to domain-specific concepts using heuristics that try to
capture morphological variation but that do not scale to other domains nor to
language variation (e.g., morphology, synonyms, paraphrasing ). In this work
the semantic decoder is trained using unaligned semantic annotations and it
uses distributed semantic representation learning to overcome the limitations
of explicit delexicalisation. The proposed architecture uses a convolutional
neural network for the sentence representation and a long-short term memory
network for the context representation. Results are presented for the publicly
available DSTC2 corpus and an In-car corpus which is similar to DSTC2 but has a
significantly higher word error rate (WER).

本文提出了一种深度学习的体系结构，用于基于轮询槽位的语音问答系统中的语义解码器组件，该语义解码器利用分布式语义表示学习来克服显式去词汇化的限制，通过使用卷积神经网络和长短时记忆网络对句子和上下文表示进行建模，并基于 DSTC2 和 In-car 数据集，对模型进行了实验验证。