InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields
Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolutions and hinder the geometric detail recovery. This paper introduces InfiniDepth, which represents depth as neural implicit fields. Through a simple yet effective local implicit decoder, we can query depth at continuous 2D coordinates, enabling arbitrary-resolution and fine-grained depth estimation. To better assess our method's capabilities, we curate a high-quality 4K synthetic benchmark from five different games, spanning diverse scenes with rich geometric and appearance details. Extensive experiments demonstrate that InfiniDepth achieves state-of-the-art performance on both synthetic and real-world benchmarks across relative and metric depth estimation tasks, particularly excelling in fine-detail regions. It also benefits the task of novel view synthesis under large viewpoint shifts, producing high-quality results with fewer holes and artifacts.
695dc956c03d6d81e4399ea4
Hao Yu
695dc956c03d6d81e4399ea5
Haotong Lin
admin_assigned
2026-01-07T13:15:04.783Z
6489a01b8de3f9d810b0154f
/avatars/f7a0fc6816535945e11bac1212dd7b57.svg
Haotong Lin
haotongl
user
haotongl
695dc956c03d6d81e4399ea6
Jiawei Wang
695dc956c03d6d81e4399ea7
Jiaxin Li
695dc956c03d6d81e4399ea8
Yida Wang
695dc956c03d6d81e4399ea9
Xueyang Zhang
admin_assigned
2026-01-07T13:15:39.946Z
6791a6c19ce382eae861ed61
https://cdn-avatars.huggingface.co/v1/production/uploads/6791a6c19ce382eae861ed61/zerctN-RdeP4hSrWidtyN.jpeg
Xueyang Zhang
zhangxueyang001
user
zhangxueyang001
695dc956c03d6d81e4399eaa
Yue Wang
695dc956c03d6d81e4399eab
Xiaowei Zhou
695dc956c03d6d81e4399eac
Ruizhen Hu
695dc956c03d6d81e4399ead
Sida Peng
admin_assigned
2026-01-07T13:16:12.074Z
62986ca2b58e71e2ac9b8f01
/avatars/83944db5f3dbb6f47c47c46fb2cb2849.svg
Sida Peng
pengsida
user
pengsida
InfiniDepth: Arbitrary-Resolution and Fine-Grained Depth Estimation with Neural Implicit Fields
Existing depth estimation methods are fundamentally limited to predicting depth on discrete image grids. Such representations restrict their scalability to arbitrary output resolutions and hinder the geometric detail recovery. This paper introduces InfiniDepth, which represents depth as neural implicit fields. Through a simple yet effective local implicit decoder, we can query depth at continuous 2D coordinates, enabling arbitrary-resolution and fine-grained depth estimation. To better assess our method's capabilities, we curate a high-quality 4K synthetic benchmark from five different games, spanning diverse scenes with rich geometric and appearance details. Extensive experiments demonstrate that InfiniDepth achieves state-of-the-art performance on both synthetic and real-world benchmarks across relative and metric depth estimation tasks, particularly excelling in fine-detail regions. It also benefits the task of novel view synthesis under large viewpoint shifts, producing high-quality results with fewer holes and artifacts.
InfiniDepth represents depth as neural implicit fields using a local implicit decoder, enabling continuous 2D coordinate querying for arbitrary-resolution depth estimation and superior performance in fine-detail regions.
neural implicit fields local implicit decoder continuous 2D coordinates arbitrary-resolution depth estimation synthetic benchmark 4K synthetic benchmark novel view synthesis viewpoint shifts