""" Most of the code is taken from https://github.com/andyzeng/tsdf-fusion-python/blob/master/fusion.py @inproceedings{zeng20163dmatch, title={3DMatch: Learning Local Geometric Descriptors from RGB-D Reconstructions}, author={Zeng, Andy and Song, Shuran and Nie{\ss}ner, Matthias and Fisher, Matthew and Xiao, Jianxiong and Funkhouser, Thomas}, booktitle={CVPR}, year={2017} } """ import numpy as np from numba import njit, prange from skimage import measure FUSION_GPU_MODE = 0 class TSDFVolume: """Volumetric TSDF Fusion of RGB-D Images.""" def __init__(self, vol_bnds, voxel_size, use_gpu=True): """Constructor. Args: vol_bnds (ndarray): An ndarray of shape (3, 2). Specifies the xyz bounds (min/max) in meters. voxel_size (float): The volume discretization in meters. """ vol_bnds = np.asarray(vol_bnds) assert vol_bnds.shape == (3, 2), "[!] `vol_bnds` should be of shape (3, 2)." # Define voxel volume parameters self._vol_bnds = vol_bnds self._voxel_size = float(voxel_size) self._trunc_margin = 5 * self._voxel_size # truncation on SDF # self._trunc_margin = 10 # truncation on SDF self._color_const = 256 * 256 # Adjust volume bounds and ensure C-order contiguous self._vol_dim = ( np.ceil((self._vol_bnds[:, 1] - self._vol_bnds[:, 0]) / self._voxel_size) .copy(order="C") .astype(int) ) self._vol_bnds[:, 1] = self._vol_bnds[:, 0] + self._vol_dim * self._voxel_size self._vol_origin = self._vol_bnds[:, 0].copy(order="C").astype(np.float32) print( "Voxel volume size: {} x {} x {} - # points: {:,}".format( self._vol_dim[0], self._vol_dim[1], self._vol_dim[2], self._vol_dim[0] * self._vol_dim[1] * self._vol_dim[2], ) ) # Initialize pointers to voxel volume in CPU memory self._tsdf_vol_cpu = np.zeros(self._vol_dim).astype(np.float32) # for computing the cumulative moving average of observations per voxel self._weight_vol_cpu = np.zeros(self._vol_dim).astype(np.float32) self._color_vol_cpu = np.zeros(self._vol_dim).astype(np.float32) self.gpu_mode = use_gpu and FUSION_GPU_MODE # Copy voxel volumes to GPU if self.gpu_mode: self._tsdf_vol_gpu = cuda.mem_alloc(self._tsdf_vol_cpu.nbytes) cuda.memcpy_htod(self._tsdf_vol_gpu, self._tsdf_vol_cpu) self._weight_vol_gpu = cuda.mem_alloc(self._weight_vol_cpu.nbytes) cuda.memcpy_htod(self._weight_vol_gpu, self._weight_vol_cpu) self._color_vol_gpu = cuda.mem_alloc(self._color_vol_cpu.nbytes) cuda.memcpy_htod(self._color_vol_gpu, self._color_vol_cpu) # Cuda kernel function (C++) self._cuda_src_mod = SourceModule( """ __global__ void integrate(float * tsdf_vol, float * weight_vol, float * color_vol, float * vol_dim, float * vol_origin, float * cam_intr, float * cam_pose, float * other_params, float * color_im, float * depth_im) { // Get voxel index int gpu_loop_idx = (int) other_params[0]; int max_threads_per_block = blockDim.x; int block_idx = blockIdx.z*gridDim.y*gridDim.x+blockIdx.y*gridDim.x+blockIdx.x; int voxel_idx = gpu_loop_idx*gridDim.x*gridDim.y*gridDim.z*max_threads_per_block+block_idx*max_threads_per_block+threadIdx.x; int vol_dim_x = (int) vol_dim[0]; int vol_dim_y = (int) vol_dim[1]; int vol_dim_z = (int) vol_dim[2]; if (voxel_idx > vol_dim_x*vol_dim_y*vol_dim_z) return; // Get voxel grid coordinates (note: be careful when casting) float voxel_x = floorf(((float)voxel_idx)/((float)(vol_dim_y*vol_dim_z))); float voxel_y = floorf(((float)(voxel_idx-((int)voxel_x)*vol_dim_y*vol_dim_z))/((float)vol_dim_z)); float voxel_z = (float)(voxel_idx-((int)voxel_x)*vol_dim_y*vol_dim_z-((int)voxel_y)*vol_dim_z); // Voxel grid coordinates to world coordinates float voxel_size = other_params[1]; float pt_x = vol_origin[0]+voxel_x*voxel_size; float pt_y = vol_origin[1]+voxel_y*voxel_size; float pt_z = vol_origin[2]+voxel_z*voxel_size; // World coordinates to camera coordinates float tmp_pt_x = pt_x-cam_pose[0*4+3]; float tmp_pt_y = pt_y-cam_pose[1*4+3]; float tmp_pt_z = pt_z-cam_pose[2*4+3]; float cam_pt_x = cam_pose[0*4+0]*tmp_pt_x+cam_pose[1*4+0]*tmp_pt_y+cam_pose[2*4+0]*tmp_pt_z; float cam_pt_y = cam_pose[0*4+1]*tmp_pt_x+cam_pose[1*4+1]*tmp_pt_y+cam_pose[2*4+1]*tmp_pt_z; float cam_pt_z = cam_pose[0*4+2]*tmp_pt_x+cam_pose[1*4+2]*tmp_pt_y+cam_pose[2*4+2]*tmp_pt_z; // Camera coordinates to image pixels int pixel_x = (int) roundf(cam_intr[0*3+0]*(cam_pt_x/cam_pt_z)+cam_intr[0*3+2]); int pixel_y = (int) roundf(cam_intr[1*3+1]*(cam_pt_y/cam_pt_z)+cam_intr[1*3+2]); // Skip if outside view frustum int im_h = (int) other_params[2]; int im_w = (int) other_params[3]; if (pixel_x < 0 || pixel_x >= im_w || pixel_y < 0 || pixel_y >= im_h || cam_pt_z<0) return; // Skip invalid depth float depth_value = depth_im[pixel_y*im_w+pixel_x]; if (depth_value == 0) return; // Integrate TSDF float trunc_margin = other_params[4]; float depth_diff = depth_value-cam_pt_z; if (depth_diff < -trunc_margin) return; float dist = fmin(1.0f,depth_diff/trunc_margin); float w_old = weight_vol[voxel_idx]; float obs_weight = other_params[5]; float w_new = w_old + obs_weight; weight_vol[voxel_idx] = w_new; tsdf_vol[voxel_idx] = (tsdf_vol[voxel_idx]*w_old+obs_weight*dist)/w_new; // Integrate color float old_color = color_vol[voxel_idx]; float old_b = floorf(old_color/(256*256)); float old_g = floorf((old_color-old_b*256*256)/256); float old_r = old_color-old_b*256*256-old_g*256; float new_color = color_im[pixel_y*im_w+pixel_x]; float new_b = floorf(new_color/(256*256)); float new_g = floorf((new_color-new_b*256*256)/256); float new_r = new_color-new_b*256*256-new_g*256; new_b = fmin(roundf((old_b*w_old+obs_weight*new_b)/w_new),255.0f); new_g = fmin(roundf((old_g*w_old+obs_weight*new_g)/w_new),255.0f); new_r = fmin(roundf((old_r*w_old+obs_weight*new_r)/w_new),255.0f); color_vol[voxel_idx] = new_b*256*256+new_g*256+new_r; }""" ) self._cuda_integrate = self._cuda_src_mod.get_function("integrate") # Determine block/grid size on GPU gpu_dev = cuda.Device(0) self._max_gpu_threads_per_block = gpu_dev.MAX_THREADS_PER_BLOCK n_blocks = int( np.ceil( float(np.prod(self._vol_dim)) / float(self._max_gpu_threads_per_block) ) ) grid_dim_x = min(gpu_dev.MAX_GRID_DIM_X, int(np.floor(np.cbrt(n_blocks)))) grid_dim_y = min( gpu_dev.MAX_GRID_DIM_Y, int(np.floor(np.sqrt(n_blocks / grid_dim_x))) ) grid_dim_z = min( gpu_dev.MAX_GRID_DIM_Z, int(np.ceil(float(n_blocks) / float(grid_dim_x * grid_dim_y))), ) self._max_gpu_grid_dim = np.array( [grid_dim_x, grid_dim_y, grid_dim_z] ).astype(int) self._n_gpu_loops = int( np.ceil( float(np.prod(self._vol_dim)) / float( np.prod(self._max_gpu_grid_dim) * self._max_gpu_threads_per_block ) ) ) else: # Get voxel grid coordinates xv, yv, zv = np.meshgrid( range(self._vol_dim[0]), range(self._vol_dim[1]), range(self._vol_dim[2]), indexing="ij", ) self.vox_coords = ( np.concatenate( [xv.reshape(1, -1), yv.reshape(1, -1), zv.reshape(1, -1)], axis=0 ) .astype(int) .T ) @staticmethod @njit(parallel=True) def vox2world(vol_origin, vox_coords, vox_size, offsets=(0.5, 0.5, 0.5)): """Convert voxel grid coordinates to world coordinates.""" vol_origin = vol_origin.astype(np.float32) vox_coords = vox_coords.astype(np.float32) # print(np.min(vox_coords)) cam_pts = np.empty_like(vox_coords, dtype=np.float32) for i in prange(vox_coords.shape[0]): for j in range(3): cam_pts[i, j] = ( vol_origin[j] + (vox_size * vox_coords[i, j]) + vox_size * offsets[j] ) return cam_pts @staticmethod @njit(parallel=True) def cam2pix(cam_pts, intr): """Convert camera coordinates to pixel coordinates.""" intr = intr.astype(np.float32) fx, fy = intr[0, 0], intr[1, 1] cx, cy = intr[0, 2], intr[1, 2] pix = np.empty((cam_pts.shape[0], 2), dtype=np.int64) for i in prange(cam_pts.shape[0]): pix[i, 0] = int(np.round((cam_pts[i, 0] * fx / cam_pts[i, 2]) + cx)) pix[i, 1] = int(np.round((cam_pts[i, 1] * fy / cam_pts[i, 2]) + cy)) return pix @staticmethod @njit(parallel=True) def integrate_tsdf(tsdf_vol, dist, w_old, obs_weight): """Integrate the TSDF volume.""" tsdf_vol_int = np.empty_like(tsdf_vol, dtype=np.float32) # print(tsdf_vol.shape) w_new = np.empty_like(w_old, dtype=np.float32) for i in prange(len(tsdf_vol)): w_new[i] = w_old[i] + obs_weight tsdf_vol_int[i] = (w_old[i] * tsdf_vol[i] + obs_weight * dist[i]) / w_new[i] return tsdf_vol_int, w_new def integrate(self, color_im, depth_im, cam_intr, cam_pose, obs_weight=1.0): """Integrate an RGB-D frame into the TSDF volume. Args: color_im (ndarray): An RGB image of shape (H, W, 3). depth_im (ndarray): A depth image of shape (H, W). cam_intr (ndarray): The camera intrinsics matrix of shape (3, 3). cam_pose (ndarray): The camera pose (i.e. extrinsics) of shape (4, 4). obs_weight (float): The weight to assign for the current observation. A higher value """ im_h, im_w = depth_im.shape # Fold RGB color image into a single channel image color_im = color_im.astype(np.float32) color_im = np.floor( color_im[..., 2] * self._color_const + color_im[..., 1] * 256 + color_im[..., 0] ) if self.gpu_mode: # GPU mode: integrate voxel volume (calls CUDA kernel) for gpu_loop_idx in range(self._n_gpu_loops): self._cuda_integrate( self._tsdf_vol_gpu, self._weight_vol_gpu, self._color_vol_gpu, cuda.InOut(self._vol_dim.astype(np.float32)), cuda.InOut(self._vol_origin.astype(np.float32)), cuda.InOut(cam_intr.reshape(-1).astype(np.float32)), cuda.InOut(cam_pose.reshape(-1).astype(np.float32)), cuda.InOut( np.asarray( [ gpu_loop_idx, self._voxel_size, im_h, im_w, self._trunc_margin, obs_weight, ], np.float32, ) ), cuda.InOut(color_im.reshape(-1).astype(np.float32)), cuda.InOut(depth_im.reshape(-1).astype(np.float32)), block=(self._max_gpu_threads_per_block, 1, 1), grid=( int(self._max_gpu_grid_dim[0]), int(self._max_gpu_grid_dim[1]), int(self._max_gpu_grid_dim[2]), ), ) else: # CPU mode: integrate voxel volume (vectorized implementation) # Convert voxel grid coordinates to pixel coordinates cam_pts = self.vox2world( self._vol_origin, self.vox_coords, self._voxel_size ) cam_pts = rigid_transform(cam_pts, np.linalg.inv(cam_pose)) pix_z = cam_pts[:, 2] pix = self.cam2pix(cam_pts, cam_intr) pix_x, pix_y = pix[:, 0], pix[:, 1] # Eliminate pixels outside view frustum valid_pix = np.logical_and( pix_x >= 0, np.logical_and( pix_x < im_w, np.logical_and(pix_y >= 0, np.logical_and(pix_y < im_h, pix_z > 0)), ), ) depth_val = np.zeros(pix_x.shape) depth_val[valid_pix] = depth_im[pix_y[valid_pix], pix_x[valid_pix]] # Integrate TSDF depth_diff = depth_val - pix_z valid_pts = np.logical_and(depth_val > 0, depth_diff >= -10) dist = depth_diff valid_vox_x = self.vox_coords[valid_pts, 0] valid_vox_y = self.vox_coords[valid_pts, 1] valid_vox_z = self.vox_coords[valid_pts, 2] w_old = self._weight_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] tsdf_vals = self._tsdf_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] valid_dist = dist[valid_pts] tsdf_vol_new, w_new = self.integrate_tsdf( tsdf_vals, valid_dist, w_old, obs_weight ) self._weight_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] = w_new self._tsdf_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] = tsdf_vol_new # Integrate color old_color = self._color_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] old_b = np.floor(old_color / self._color_const) old_g = np.floor((old_color - old_b * self._color_const) / 256) old_r = old_color - old_b * self._color_const - old_g * 256 new_color = color_im[pix_y[valid_pts], pix_x[valid_pts]] new_b = np.floor(new_color / self._color_const) new_g = np.floor((new_color - new_b * self._color_const) / 256) new_r = new_color - new_b * self._color_const - new_g * 256 new_b = np.minimum( 255.0, np.round((w_old * old_b + obs_weight * new_b) / w_new) ) new_g = np.minimum( 255.0, np.round((w_old * old_g + obs_weight * new_g) / w_new) ) new_r = np.minimum( 255.0, np.round((w_old * old_r + obs_weight * new_r) / w_new) ) self._color_vol_cpu[valid_vox_x, valid_vox_y, valid_vox_z] = ( new_b * self._color_const + new_g * 256 + new_r ) def get_volume(self): if self.gpu_mode: cuda.memcpy_dtoh(self._tsdf_vol_cpu, self._tsdf_vol_gpu) cuda.memcpy_dtoh(self._color_vol_cpu, self._color_vol_gpu) return self._tsdf_vol_cpu, self._color_vol_cpu def get_point_cloud(self): """Extract a point cloud from the voxel volume.""" tsdf_vol, color_vol = self.get_volume() # Marching cubes verts = measure.marching_cubes_lewiner(tsdf_vol, level=0)[0] verts_ind = np.round(verts).astype(int) verts = verts * self._voxel_size + self._vol_origin # Get vertex colors rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]] colors_b = np.floor(rgb_vals / self._color_const) colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256) colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256 colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T colors = colors.astype(np.uint8) pc = np.hstack([verts, colors]) return pc def get_mesh(self): """Compute a mesh from the voxel volume using marching cubes.""" tsdf_vol, color_vol = self.get_volume() # Marching cubes verts, faces, norms, vals = measure.marching_cubes_lewiner(tsdf_vol, level=0) verts_ind = np.round(verts).astype(int) verts = ( verts * self._voxel_size + self._vol_origin ) # voxel grid coordinates to world coordinates # Get vertex colors rgb_vals = color_vol[verts_ind[:, 0], verts_ind[:, 1], verts_ind[:, 2]] colors_b = np.floor(rgb_vals / self._color_const) colors_g = np.floor((rgb_vals - colors_b * self._color_const) / 256) colors_r = rgb_vals - colors_b * self._color_const - colors_g * 256 colors = np.floor(np.asarray([colors_r, colors_g, colors_b])).T colors = colors.astype(np.uint8) return verts, faces, norms, colors def rigid_transform(xyz, transform): """Applies a rigid transform to an (N, 3) pointcloud.""" xyz_h = np.hstack([xyz, np.ones((len(xyz), 1), dtype=np.float32)]) xyz_t_h = np.dot(transform, xyz_h.T).T return xyz_t_h[:, :3] def get_view_frustum(depth_im, cam_intr, cam_pose): """Get corners of 3D camera view frustum of depth image""" im_h = depth_im.shape[0] im_w = depth_im.shape[1] max_depth = np.max(depth_im) view_frust_pts = np.array( [ (np.array([0, 0, 0, im_w, im_w]) - cam_intr[0, 2]) * np.array([0, max_depth, max_depth, max_depth, max_depth]) / cam_intr[0, 0], (np.array([0, 0, im_h, 0, im_h]) - cam_intr[1, 2]) * np.array([0, max_depth, max_depth, max_depth, max_depth]) / cam_intr[1, 1], np.array([0, max_depth, max_depth, max_depth, max_depth]), ] ) view_frust_pts = rigid_transform(view_frust_pts.T, cam_pose).T return view_frust_pts def meshwrite(filename, verts, faces, norms, colors): """Save a 3D mesh to a polygon .ply file.""" # Write header ply_file = open(filename, "w") ply_file.write("ply\n") ply_file.write("format ascii 1.0\n") ply_file.write("element vertex %d\n" % (verts.shape[0])) ply_file.write("property float x\n") ply_file.write("property float y\n") ply_file.write("property float z\n") ply_file.write("property float nx\n") ply_file.write("property float ny\n") ply_file.write("property float nz\n") ply_file.write("property uchar red\n") ply_file.write("property uchar green\n") ply_file.write("property uchar blue\n") ply_file.write("element face %d\n" % (faces.shape[0])) ply_file.write("property list uchar int vertex_index\n") ply_file.write("end_header\n") # Write vertex list for i in range(verts.shape[0]): ply_file.write( "%f %f %f %f %f %f %d %d %d\n" % ( verts[i, 0], verts[i, 1], verts[i, 2], norms[i, 0], norms[i, 1], norms[i, 2], colors[i, 0], colors[i, 1], colors[i, 2], ) ) # Write face list for i in range(faces.shape[0]): ply_file.write("3 %d %d %d\n" % (faces[i, 0], faces[i, 1], faces[i, 2])) ply_file.close() def pcwrite(filename, xyzrgb): """Save a point cloud to a polygon .ply file.""" xyz = xyzrgb[:, :3] rgb = xyzrgb[:, 3:].astype(np.uint8) # Write header ply_file = open(filename, "w") ply_file.write("ply\n") ply_file.write("format ascii 1.0\n") ply_file.write("element vertex %d\n" % (xyz.shape[0])) ply_file.write("property float x\n") ply_file.write("property float y\n") ply_file.write("property float z\n") ply_file.write("property uchar red\n") ply_file.write("property uchar green\n") ply_file.write("property uchar blue\n") ply_file.write("end_header\n") # Write vertex list for i in range(xyz.shape[0]): ply_file.write( "%f %f %f %d %d %d\n" % ( xyz[i, 0], xyz[i, 1], xyz[i, 2], rgb[i, 0], rgb[i, 1], rgb[i, 2], ) )