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.     «

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 movem...     »