Point clouds continue to be acquired with greater accuracy and less occlusion over complex scenes, characterised by high roughness and topographic variation in all three dimensions. The most widely adopted approach to change detection, M3C2, measures change along the local surface normal, which varies between points and bypasses the uncertainties involved in mesh or DEM generation. While adaptive, this direction of comparison is nevertheless user-defined and becomes less relevant where the movement direction deviates from the surface normal. Measured change therefore also becomes less meaningful, as it is a projection onto this direction. Sliding of a failing slope, for example, is predominantly surface parallel rather than along the surface normal. We present an approach that derives a dominant movement direction (DMD) at each point based on multi-scale, multi-directional change quantifications. The DMDs differ from the surface normals in three LiDAR-derived test cases; a rockfall, an avalanche, and rock glacier movement, providing more accurate measures of rockfall depth and boulder movement across the rock glacier. When the direction of change detection is orthogonal to local relief (i.e. across the surface), a variable length search cylinder that intersects only a single (corresponding) surface is necessary during change detection. Where movement results in new regions of occlusion in the second point cloud, we show that the proportion of points for which no valid change could be recorded decreases by up to 15% using the DMD rather than the surface normal. We emphasise the importance of examining the direction over which change is measured, and highlight that a comparison direction that adapts to movement rather than to the local surface can provide more relevant and accurate measures of change where the movement is not orthogonal to the surface. Our approach represents a supplementary tool for cloud-to-cloud comparison, where a choice of tool should be made based on the expected DMD deviation from the surface normal.

在复杂场景上，点云将继续以更高的精度和更少的遮挡来获取，其特征在于所有三个维度上的高粗糙度和地形变化。最广泛采用的变化检测方法M3C2沿局部表面法线测量变化，该变化在点之间变化，并绕开了网格或DEM生成所涉及的不确定性。尽管是自适应的，但是该比较方向是用户定义的，并且在移动方向偏离表面法线的地方变得不那么重要。因此，测得的变化也没有太大意义，因为它是该方向的投影。例如，失效斜坡的滑动主要是表面平行，而不是沿着表面法线。我们提出了一种基于多尺度，多方向变化量化推导每个点的主导运动方向（DMD）的方法。在三个源自LiDAR的测试案例中，DMD与表面法线有所不同。落石，雪崩和岩石冰川运动，从而可以更准确地测量落石深度和横跨冰川的巨石运动。当变化检测的方向与局部起伏正交（即横跨整个表面）时，在变化检测期间仅需要与单个（对应）表面相交的可变长度搜索圆柱体是必要的。在第二点云中运动导致新的遮挡区域的情况下，我们表明，使用DMD而不是表面法线，无法记录有效变化的点比例最多降低15％。我们强调了检查变化变化方向的重要性，并强调了适应于运动而不是局部表面的比较方向可以在运动不垂直于表面的情况下提供更相关和准确的变化量度。我们的方法代表了一种用于云与云比较的补充工具，其中应基于与表面法线的预期DMD偏差来选择工具。