Real-world objects and environments are predominantly composed of edge
features, including straight lines and curves. Such edges are crucial elements
for various applications, such as CAD modeling, surface meshing, lane mapping,
etc. However, existing traditional methods only prioritize lines over curves
for simplicity in geometric modeling. To this end, we introduce EMAP, a new
method for learning 3D edge representations with a focus on both lines and
curves. Our method implicitly encodes 3D edge distance and direction in
Unsigned Distance Functions (UDF) from multi-view edge maps. On top of this
neural representation, we propose an edge extraction algorithm that robustly
abstracts parametric 3D edges from the inferred edge points and their
directions. Comprehensive evaluations demonstrate that our method achieves
better 3D edge reconstruction on multiple challenging datasets. We further show
that our learned UDF field enhances neural surface reconstruction by capturing
more details.

Real-world objects and environments predominantly consist of edge features. In this paper, the authors propose EMAP, a method for learning 3D edge representations using both lines and curves, utilizing Unsigned Distance Functions (UDF) from multi-view edge maps. The method achieves better 3D edge reconstruction and enhances neural surface reconstruction by capturing more details.

3D 神经边缘重建

3D Neural Edge Reconstruction

Modeling expressive cross-modal interactions seems crucial in multimodal
tasks, such as visual question answering. However, sometimes high-performing
black-box algorithms turn out to be mostly exploiting unimodal signals in the
data. We propose a new diagnostic tool, empirical multimodally-additive
function projection (EMAP), for isolating whether or not cross-modal
interactions improve performance for a given model on a given task. This
function projection modifies model predictions so that cross-modal interactions
are eliminated, isolating the additive, unimodal structure. For seven
image+text classification tasks (on each of which we set new state-of-the-art
benchmarks), we find that, in many cases, removing cross-modal interactions
results in little to no performance degradation. Surprisingly, this holds even
when expressive models, with capacity to consider interactions, otherwise
outperform less expressive models; thus, performance improvements, even when
present, often cannot be attributed to consideration of cross-modal feature
interactions. We hence recommend that researchers in multimodal machine
learning report the performance not only of unimodal baselines, but also the
EMAP of their best-performing model.

提出一种名为 EMAP 的诊断工具，用于检测跨模态交互是否提高给定模型在给定任务上的性能，在七个图像 + 文本分类任务中发现，即使具有考虑交互作用的表达模型在表现上优于不太表达的模型，删除跨模态交互也往往不会降低性能。建议多模式机器学习研究人员不仅报告单模基线的性能，而且还要报告最佳性能模型的 EMAP。