根据Transformer的原论文,self-attention可以被表示为:yi=∑xj∈Xρ(φ(xi)Tψ(xj)+δ)α(xj),其中, X 是特征向量的集合, yi 是输出特征, φ,ψ,α 是逐点特征变换, δ 是位置编码函数, ρ 为归一化函数如softmax。 在此基础上,2020年的CVPR[2]又提出了vector attention:yi=∑xj∈Xρ(γ(β(...
本文的出发点在于,基于Self-attention(自注意力)机制的网络已经在NLP和图像分析领域取得了巨大的成功,探究自注意力机制在点云数据上的应用。同时作者认为,自注意力机制是天生适合点云数据的,因为其本质就是点集操作。 论文链接: http://openaccess.thecvf.com/content/ICCV2021/papers/Zhao_Point_Transformer_ICCV_...
Here, the purpose of Point Self-Attention kernel block (PSA) is to adaptively aggregate local neighboring point features with learned relations between neighboring points, which can be formulated as, yi=∑j∈N(i)α[xN(i)]j⋅β(xj) where xN(i) is the group of point feature vectors for...
direct someone's gaze or attention towards something, especially by extending one's finger (为引人注目或注意而用手指)指出 [reporting verb]say something to make someone aware of a fact or circumstance 指出,指明 [with clause]she pointed out that his van had been in the car park all day. ...
如上图所示，Encoder由多头自注意力层(multi-headself-attention layer)与前馈神经网络(feed-forward network)组成，Decoder则由多头自注意力层、编码器解码器交叉注意力层和前馈神经网络构成。几何敏感的Transformer：我们针对点云输入设计了一种即插即用的新型transformer block。在原本的transformer 模块中，网络只利用自...
It applies self-attention with k = 16 nearest neighbors to local neighborhoods, aligning with NCBL calculations for consistent self-attention regularization in local contexts. NCBL-PT improves 3D segmentation at class intersections, as evidenced by a 3.31% increase in Intersection over Union (IOU) ...
Without any self-attention modules, OA-CNNs favorably surpass point transformers in terms of accuracy in both indoor and outdoor scenes, with much less latency and memory cost. Issue related to OA-CNNs can @Pbihao.# ScanNet sh scripts/train.sh -g 4 -d scannet -c semseg-oacnns-v1m1-...
置换不变性：CSANet能够处理无序且非结构化的原始点云数据，同时保持置换不变性。这意味着，无论点云中点的顺序如何变化，CSANet都能产生一致的结果。多尺度融合模块：为了捕捉不同尺度的特征，CSANet引入了多尺度融合模块。该模块能够自适应地融合不同尺度的信息，并通过快速下降分支提供丰富的梯度信息，...
NonLocal本质上是使用Self-Attention,对每个采样所得点,在整个点云范围内进行feature融合,为每个采样点计算出一个包含全局信息的feature。流程图如下: 其中: ·Query Points:用FPS在前一层点云中采样,并用Adaptive Sampling调整后所得的点云feature; ·Key Points:前一层点云的feature。
the self-attentiontransformer is introduced to consume the point clouds.We develop Point Attention Transformers (PATs), using aparameter-eff i cient Group Shuff l e Attention (GSA) to re-place the costly Multi-Head Attention. We demonstrate itsability to process size-varying inputs, and prove ...