Sparse SPN: Depth Completion from Sparse Keypoints
Our long term goal is to use image-based depth completion to quickly create 3D models from sparse point clouds, e.g. from SfM or SLAM. Much progress has been made in depth completion. However, most current works assume well distributed samples of known depth, e.g. Lidar or random uniform sampling, and perform poorly on uneven samples, such as from keypoints, due to the large unsampled regions. To address this problem, we extend CSPN with multiscale prediction and a dilated kernel, leading to much better completion of keypoint-sampled depth. We also show that a model trained on NYUv2 creates surprisingly good point clouds on ETH3D by completing sparse SfM points.
Code (0)
등록된 구현이 없습니다.
Tasks
Depth CompletionSimilar Papers 제목 키워드 기반
Masked Spatial Propagation Network for Sparsity-Adaptive Depth Refinement
The main function of depth completion is to compensate for an insufficient and unpredictable number of sparse depth measurements of hardware sensors. However, existing research on depth completion assumes that the sparsi…
Depth CompletionMarigold-DC: Zero-Shot Monocular Depth Completion with Guided Diffusion
Depth completion upgrades sparse depth measurements into dense depth maps guided by a conventional image. Existing methods for this highly ill-posed task operate in tightly constrained settings and tend to struggle when …
DenoisingDepth CompletionDepth EstimationMonocular Depth Estimation+1GAC-Net_Geometric and attention-based Network for Depth Completion
Depth completion is a key task in autonomous driving, aiming to complete sparse LiDAR depth measurements into high-quality dense depth maps through image guidance. However, existing methods usually treat depth maps as an…
Autonomous DrivingDepth CompletionFrom Depth What Can You See? Depth Completion via Auxiliary Image Reconstruction
Depth completion recovers dense depth from sparse measurements, e.g., LiDAR. Existing depth-only methods use sparse depth as the only input. However, these methods may fail to recover semantics consistent boundaries, or …
Depth CompletionImage ReconstructionSparse and Dense Data with CNNs: Depth Completion and Semantic Segmentation
Convolutional neural networks are designed for dense data, but vision data is often sparse (stereo depth, point clouds, pen stroke, etc.). We present a method to handle sparse depth data with optional dense RGB, and acco…
Depth CompletionSemantic Segmentation